Model 1
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AERNASE.clin.targets)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
cat(paste0(" > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.targets.survival.",ep,".2G.",
TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 1 (Simple model)
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age+Gender + ORdate_year, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age+Gender + ORdate_year, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.targets.Cox.",ep,".2G.",
# Today,".AERNASE.clin.targets.Cox.",ep,".4G.",
TRAITS.TARGET.RANK[target_of_interest],".MODEL1.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.targets", ep, TRAITS.TARGET.RANK[target_of_interest])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 2.867e-01 1.332e+00 2.374e-01 1.207 0.2273
Age 3.447e-02 1.035e+00 1.378e-02 2.502 0.0124 *
Gendermale 8.335e-01 2.301e+00 3.263e-01 2.555 0.0106 *
ORdate_year2002 1.605e+01 9.319e+06 8.546e+03 0.002 0.9985
ORdate_year2003 1.486e+01 2.838e+06 8.546e+03 0.002 0.9986
ORdate_year2004 1.517e+01 3.882e+06 8.546e+03 0.002 0.9986
ORdate_year2005 1.553e+01 5.536e+06 8.546e+03 0.002 0.9986
ORdate_year2006 1.551e+01 5.443e+06 8.546e+03 0.002 0.9986
ORdate_year2007 1.470e+01 2.419e+06 8.546e+03 0.002 0.9986
ORdate_year2008 1.574e+01 6.886e+06 8.546e+03 0.002 0.9985
ORdate_year2009 1.493e+01 3.033e+06 8.546e+03 0.002 0.9986
ORdate_year2010 1.534e+01 4.595e+06 8.546e+03 0.002 0.9986
ORdate_year2011 1.444e+01 1.874e+06 8.546e+03 0.002 0.9987
ORdate_year2012 1.515e+01 3.785e+06 8.546e+03 0.002 0.9986
ORdate_year2013 -9.919e-01 3.709e-01 9.337e+03 0.000 0.9999
ORdate_year2014 -1.015e+00 3.625e-01 9.855e+03 0.000 0.9999
ORdate_year2015 -7.694e-02 9.260e-01 9.689e+03 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.332e+00 7.508e-01 0.8364 2.121
Age 1.035e+00 9.661e-01 1.0075 1.063
Gendermale 2.301e+00 4.345e-01 1.2141 4.362
ORdate_year2002 9.319e+06 1.073e-07 0.0000 Inf
ORdate_year2003 2.838e+06 3.524e-07 0.0000 Inf
ORdate_year2004 3.882e+06 2.576e-07 0.0000 Inf
ORdate_year2005 5.536e+06 1.806e-07 0.0000 Inf
ORdate_year2006 5.443e+06 1.837e-07 0.0000 Inf
ORdate_year2007 2.419e+06 4.133e-07 0.0000 Inf
ORdate_year2008 6.886e+06 1.452e-07 0.0000 Inf
ORdate_year2009 3.033e+06 3.297e-07 0.0000 Inf
ORdate_year2010 4.595e+06 2.176e-07 0.0000 Inf
ORdate_year2011 1.874e+06 5.335e-07 0.0000 Inf
ORdate_year2012 3.785e+06 2.642e-07 0.0000 Inf
ORdate_year2013 3.709e-01 2.696e+00 0.0000 Inf
ORdate_year2014 3.625e-01 2.758e+00 0.0000 Inf
ORdate_year2015 9.259e-01 1.080e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.659 (se = 0.03 )
Likelihood ratio test= 28.69 on 17 df, p=0.04
Wald test = 23.34 on 17 df, p=0.1
Score (logrank) test = 26.02 on 17 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: CXCL10
Effect size...............: 0.286662
Standard error............: 0.237408
Odds ratio (effect size)..: 1.332
Lower 95% CI..............: 0.836
Upper 95% CI..............: 2.121
T-value...................: 1.207465
P-value...................: 0.2272532
Sample size in model......: 619
Number of events..........: 80
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] -5.161e-02 9.497e-01 2.594e-01 -0.199 0.8423
Age 3.321e-02 1.034e+00 1.378e-02 2.410 0.0160 *
Gendermale 8.287e-01 2.290e+00 3.264e-01 2.539 0.0111 *
ORdate_year2002 1.613e+01 1.011e+07 8.516e+03 0.002 0.9985
ORdate_year2003 1.495e+01 3.105e+06 8.516e+03 0.002 0.9986
ORdate_year2004 1.530e+01 4.428e+06 8.516e+03 0.002 0.9986
ORdate_year2005 1.566e+01 6.318e+06 8.516e+03 0.002 0.9985
ORdate_year2006 1.562e+01 6.104e+06 8.516e+03 0.002 0.9985
ORdate_year2007 1.479e+01 2.663e+06 8.516e+03 0.002 0.9986
ORdate_year2008 1.581e+01 7.370e+06 8.516e+03 0.002 0.9985
ORdate_year2009 1.506e+01 3.489e+06 8.516e+03 0.002 0.9986
ORdate_year2010 1.548e+01 5.266e+06 8.516e+03 0.002 0.9985
ORdate_year2011 1.456e+01 2.112e+06 8.516e+03 0.002 0.9986
ORdate_year2012 1.530e+01 4.399e+06 8.516e+03 0.002 0.9986
ORdate_year2013 -9.047e-01 4.047e-01 9.308e+03 0.000 0.9999
ORdate_year2014 -8.029e-01 4.480e-01 9.820e+03 0.000 0.9999
ORdate_year2015 -6.486e-02 9.372e-01 9.653e+03 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 9.497e-01 1.053e+00 0.5712 1.579
Age 1.034e+00 9.673e-01 1.0062 1.062
Gendermale 2.290e+00 4.366e-01 1.2081 4.342
ORdate_year2002 1.011e+07 9.894e-08 0.0000 Inf
ORdate_year2003 3.105e+06 3.220e-07 0.0000 Inf
ORdate_year2004 4.428e+06 2.258e-07 0.0000 Inf
ORdate_year2005 6.318e+06 1.583e-07 0.0000 Inf
ORdate_year2006 6.104e+06 1.638e-07 0.0000 Inf
ORdate_year2007 2.663e+06 3.755e-07 0.0000 Inf
ORdate_year2008 7.370e+06 1.357e-07 0.0000 Inf
ORdate_year2009 3.489e+06 2.866e-07 0.0000 Inf
ORdate_year2010 5.266e+06 1.899e-07 0.0000 Inf
ORdate_year2011 2.112e+06 4.735e-07 0.0000 Inf
ORdate_year2012 4.399e+06 2.273e-07 0.0000 Inf
ORdate_year2013 4.047e-01 2.471e+00 0.0000 Inf
ORdate_year2014 4.480e-01 2.232e+00 0.0000 Inf
ORdate_year2015 9.372e-01 1.067e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.653 (se = 0.03 )
Likelihood ratio test= 27.31 on 17 df, p=0.05
Wald test = 22.07 on 17 df, p=0.2
Score (logrank) test = 24.75 on 17 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: PCSK9
Effect size...............: -0.051607
Standard error............: 0.25938
Odds ratio (effect size)..: 0.95
Lower 95% CI..............: 0.571
Upper 95% CI..............: 1.579
T-value...................: -0.198964
P-value...................: 0.8422906
Sample size in model......: 619
Number of events..........: 80
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.193e-01 1.127e+00 2.308e-01 0.517 0.60518
Age 3.323e-02 1.034e+00 1.370e-02 2.426 0.01527 *
Gendermale 8.463e-01 2.331e+00 3.278e-01 2.581 0.00984 **
ORdate_year2002 1.620e+01 1.085e+07 8.513e+03 0.002 0.99848
ORdate_year2003 1.499e+01 3.227e+06 8.513e+03 0.002 0.99860
ORdate_year2004 1.532e+01 4.524e+06 8.513e+03 0.002 0.99856
ORdate_year2005 1.569e+01 6.506e+06 8.513e+03 0.002 0.99853
ORdate_year2006 1.565e+01 6.267e+06 8.513e+03 0.002 0.99853
ORdate_year2007 1.483e+01 2.749e+06 8.513e+03 0.002 0.99861
ORdate_year2008 1.586e+01 7.687e+06 8.513e+03 0.002 0.99851
ORdate_year2009 1.506e+01 3.482e+06 8.513e+03 0.002 0.99859
ORdate_year2010 1.550e+01 5.371e+06 8.513e+03 0.002 0.99855
ORdate_year2011 1.459e+01 2.165e+06 8.513e+03 0.002 0.99863
ORdate_year2012 1.535e+01 4.617e+06 8.513e+03 0.002 0.99856
ORdate_year2013 -8.995e-01 4.068e-01 9.304e+03 0.000 0.99992
ORdate_year2014 -8.374e-01 4.328e-01 9.814e+03 0.000 0.99993
ORdate_year2015 -5.908e-02 9.426e-01 9.638e+03 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.127e+00 8.875e-01 0.7167 1.771
Age 1.034e+00 9.673e-01 1.0064 1.062
Gendermale 2.331e+00 4.290e-01 1.2260 4.432
ORdate_year2002 1.085e+07 9.213e-08 0.0000 Inf
ORdate_year2003 3.227e+06 3.099e-07 0.0000 Inf
ORdate_year2004 4.524e+06 2.211e-07 0.0000 Inf
ORdate_year2005 6.506e+06 1.537e-07 0.0000 Inf
ORdate_year2006 6.267e+06 1.596e-07 0.0000 Inf
ORdate_year2007 2.749e+06 3.638e-07 0.0000 Inf
ORdate_year2008 7.687e+06 1.301e-07 0.0000 Inf
ORdate_year2009 3.482e+06 2.872e-07 0.0000 Inf
ORdate_year2010 5.371e+06 1.862e-07 0.0000 Inf
ORdate_year2011 2.165e+06 4.618e-07 0.0000 Inf
ORdate_year2012 4.617e+06 2.166e-07 0.0000 Inf
ORdate_year2013 4.068e-01 2.458e+00 0.0000 Inf
ORdate_year2014 4.328e-01 2.310e+00 0.0000 Inf
ORdate_year2015 9.426e-01 1.061e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.653 (se = 0.03 )
Likelihood ratio test= 27.54 on 17 df, p=0.05
Wald test = 22.14 on 17 df, p=0.2
Score (logrank) test = 24.81 on 17 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: COL4A1
Effect size...............: 0.119341
Standard error............: 0.230849
Odds ratio (effect size)..: 1.127
Lower 95% CI..............: 0.717
Upper 95% CI..............: 1.771
T-value...................: 0.516964
P-value...................: 0.6051813
Sample size in model......: 619
Number of events..........: 80
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 3.256e-01 1.385e+00 2.341e-01 1.391 0.16436
Age 3.257e-02 1.033e+00 1.371e-02 2.376 0.01752 *
Gendermale 8.755e-01 2.400e+00 3.287e-01 2.663 0.00774 **
ORdate_year2002 1.630e+01 1.204e+07 8.545e+03 0.002 0.99848
ORdate_year2003 1.508e+01 3.550e+06 8.545e+03 0.002 0.99859
ORdate_year2004 1.538e+01 4.777e+06 8.545e+03 0.002 0.99856
ORdate_year2005 1.574e+01 6.851e+06 8.545e+03 0.002 0.99853
ORdate_year2006 1.572e+01 6.689e+06 8.545e+03 0.002 0.99853
ORdate_year2007 1.497e+01 3.177e+06 8.545e+03 0.002 0.99860
ORdate_year2008 1.597e+01 8.598e+06 8.545e+03 0.002 0.99851
ORdate_year2009 1.511e+01 3.647e+06 8.545e+03 0.002 0.99859
ORdate_year2010 1.555e+01 5.682e+06 8.545e+03 0.002 0.99855
ORdate_year2011 1.465e+01 2.303e+06 8.545e+03 0.002 0.99863
ORdate_year2012 1.546e+01 5.162e+06 8.545e+03 0.002 0.99856
ORdate_year2013 -7.744e-01 4.610e-01 9.338e+03 0.000 0.99993
ORdate_year2014 -8.667e-01 4.204e-01 9.851e+03 0.000 0.99993
ORdate_year2015 -1.142e-02 9.886e-01 9.628e+03 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.385e+00 7.221e-01 0.8752 2.191
Age 1.033e+00 9.680e-01 1.0057 1.061
Gendermale 2.400e+00 4.166e-01 1.2602 4.571
ORdate_year2002 1.204e+07 8.306e-08 0.0000 Inf
ORdate_year2003 3.550e+06 2.817e-07 0.0000 Inf
ORdate_year2004 4.777e+06 2.093e-07 0.0000 Inf
ORdate_year2005 6.851e+06 1.460e-07 0.0000 Inf
ORdate_year2006 6.689e+06 1.495e-07 0.0000 Inf
ORdate_year2007 3.177e+06 3.148e-07 0.0000 Inf
ORdate_year2008 8.598e+06 1.163e-07 0.0000 Inf
ORdate_year2009 3.647e+06 2.742e-07 0.0000 Inf
ORdate_year2010 5.682e+06 1.760e-07 0.0000 Inf
ORdate_year2011 2.303e+06 4.342e-07 0.0000 Inf
ORdate_year2012 5.162e+06 1.937e-07 0.0000 Inf
ORdate_year2013 4.610e-01 2.169e+00 0.0000 Inf
ORdate_year2014 4.204e-01 2.379e+00 0.0000 Inf
ORdate_year2015 9.886e-01 1.011e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.656 (se = 0.03 )
Likelihood ratio test= 29.23 on 17 df, p=0.03
Wald test = 23.76 on 17 df, p=0.1
Score (logrank) test = 26.26 on 17 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: COL4A2
Effect size...............: 0.325553
Standard error............: 0.234117
Odds ratio (effect size)..: 1.385
Lower 95% CI..............: 0.875
Upper 95% CI..............: 2.191
T-value...................: 1.390557
P-value...................: 0.1643598
Sample size in model......: 619
Number of events..........: 80
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -1.768e-01 8.379e-01 2.316e-01 -0.764 0.4451
Age 3.369e-02 1.034e+00 1.380e-02 2.441 0.0146 *
Gendermale 8.206e-01 2.272e+00 3.265e-01 2.513 0.0120 *
ORdate_year2002 1.617e+01 1.057e+07 8.538e+03 0.002 0.9985
ORdate_year2003 1.502e+01 3.337e+06 8.538e+03 0.002 0.9986
ORdate_year2004 1.537e+01 4.725e+06 8.538e+03 0.002 0.9986
ORdate_year2005 1.575e+01 6.892e+06 8.538e+03 0.002 0.9985
ORdate_year2006 1.572e+01 6.694e+06 8.538e+03 0.002 0.9985
ORdate_year2007 1.488e+01 2.897e+06 8.538e+03 0.002 0.9986
ORdate_year2008 1.589e+01 7.972e+06 8.538e+03 0.002 0.9985
ORdate_year2009 1.518e+01 3.896e+06 8.538e+03 0.002 0.9986
ORdate_year2010 1.556e+01 5.732e+06 8.538e+03 0.002 0.9985
ORdate_year2011 1.464e+01 2.280e+06 8.538e+03 0.002 0.9986
ORdate_year2012 1.534e+01 4.575e+06 8.538e+03 0.002 0.9986
ORdate_year2013 -8.137e-01 4.432e-01 9.343e+03 0.000 0.9999
ORdate_year2014 -6.919e-01 5.006e-01 9.828e+03 0.000 0.9999
ORdate_year2015 4.366e-02 1.045e+00 9.718e+03 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 8.379e-01 1.193e+00 0.5322 1.319
Age 1.034e+00 9.669e-01 1.0067 1.063
Gendermale 2.272e+00 4.402e-01 1.1980 4.308
ORdate_year2002 1.057e+07 9.460e-08 0.0000 Inf
ORdate_year2003 3.337e+06 2.997e-07 0.0000 Inf
ORdate_year2004 4.725e+06 2.117e-07 0.0000 Inf
ORdate_year2005 6.892e+06 1.451e-07 0.0000 Inf
ORdate_year2006 6.694e+06 1.494e-07 0.0000 Inf
ORdate_year2007 2.897e+06 3.452e-07 0.0000 Inf
ORdate_year2008 7.972e+06 1.254e-07 0.0000 Inf
ORdate_year2009 3.896e+06 2.566e-07 0.0000 Inf
ORdate_year2010 5.732e+06 1.744e-07 0.0000 Inf
ORdate_year2011 2.280e+06 4.386e-07 0.0000 Inf
ORdate_year2012 4.575e+06 2.186e-07 0.0000 Inf
ORdate_year2013 4.432e-01 2.256e+00 0.0000 Inf
ORdate_year2014 5.006e-01 1.998e+00 0.0000 Inf
ORdate_year2015 1.045e+00 9.573e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.654 (se = 0.03 )
Likelihood ratio test= 27.86 on 17 df, p=0.05
Wald test = 22.68 on 17 df, p=0.2
Score (logrank) test = 25.44 on 17 df, p=0.09
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: LDLR
Effect size...............: -0.176813
Standard error............: 0.231555
Odds ratio (effect size)..: 0.838
Lower 95% CI..............: 0.532
Upper 95% CI..............: 1.319
T-value...................: -0.763588
P-value...................: 0.4451131
Sample size in model......: 619
Number of events..........: 80
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 80
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 2.459e-01 1.279e+00 2.317e-01 1.061 0.28854
Age 3.319e-02 1.034e+00 1.360e-02 2.441 0.01465 *
Gendermale 8.528e-01 2.346e+00 3.272e-01 2.606 0.00916 **
ORdate_year2002 1.630e+01 1.196e+07 8.530e+03 0.002 0.99848
ORdate_year2003 1.506e+01 3.468e+06 8.530e+03 0.002 0.99859
ORdate_year2004 1.539e+01 4.833e+06 8.530e+03 0.002 0.99856
ORdate_year2005 1.574e+01 6.846e+06 8.530e+03 0.002 0.99853
ORdate_year2006 1.569e+01 6.544e+06 8.530e+03 0.002 0.99853
ORdate_year2007 1.489e+01 2.930e+06 8.530e+03 0.002 0.99861
ORdate_year2008 1.593e+01 8.245e+06 8.530e+03 0.002 0.99851
ORdate_year2009 1.509e+01 3.584e+06 8.530e+03 0.002 0.99859
ORdate_year2010 1.554e+01 5.634e+06 8.530e+03 0.002 0.99855
ORdate_year2011 1.465e+01 2.294e+06 8.530e+03 0.002 0.99863
ORdate_year2012 1.539e+01 4.805e+06 8.530e+03 0.002 0.99856
ORdate_year2013 -7.045e-01 4.944e-01 9.327e+03 0.000 0.99994
ORdate_year2014 -8.438e-01 4.301e-01 9.833e+03 0.000 0.99993
ORdate_year2015 -2.024e-02 9.800e-01 9.628e+03 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.279e+00 7.820e-01 0.812 2.014
Age 1.034e+00 9.674e-01 1.007 1.062
Gendermale 2.346e+00 4.262e-01 1.235 4.455
ORdate_year2002 1.196e+07 8.360e-08 0.000 Inf
ORdate_year2003 3.468e+06 2.884e-07 0.000 Inf
ORdate_year2004 4.833e+06 2.069e-07 0.000 Inf
ORdate_year2005 6.846e+06 1.461e-07 0.000 Inf
ORdate_year2006 6.544e+06 1.528e-07 0.000 Inf
ORdate_year2007 2.930e+06 3.413e-07 0.000 Inf
ORdate_year2008 8.245e+06 1.213e-07 0.000 Inf
ORdate_year2009 3.584e+06 2.790e-07 0.000 Inf
ORdate_year2010 5.634e+06 1.775e-07 0.000 Inf
ORdate_year2011 2.294e+06 4.358e-07 0.000 Inf
ORdate_year2012 4.805e+06 2.081e-07 0.000 Inf
ORdate_year2013 4.943e-01 2.023e+00 0.000 Inf
ORdate_year2014 4.301e-01 2.325e+00 0.000 Inf
ORdate_year2015 9.800e-01 1.020e+00 0.000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.658 (se = 0.03 )
Likelihood ratio test= 28.41 on 17 df, p=0.04
Wald test = 22.96 on 17 df, p=0.2
Score (logrank) test = 25.59 on 17 df, p=0.08
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: CD36
Effect size...............: 0.245898
Standard error............: 0.231689
Odds ratio (effect size)..: 1.279
Lower 95% CI..............: 0.812
Upper 95% CI..............: 2.014
T-value...................: 1.061327
P-value...................: 0.2885414
Sample size in model......: 619
Number of events..........: 80
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 4.669e-02 1.048e+00 3.181e-01 0.147 0.883
Age 2.727e-02 1.028e+00 1.775e-02 1.537 0.124
Gendermale 4.187e-01 1.520e+00 3.730e-01 1.123 0.262
ORdate_year2002 1.638e+01 1.306e+07 1.139e+04 0.001 0.999
ORdate_year2003 1.533e+01 4.550e+06 1.139e+04 0.001 0.999
ORdate_year2004 1.551e+01 5.436e+06 1.139e+04 0.001 0.999
ORdate_year2005 1.603e+01 9.139e+06 1.139e+04 0.001 0.999
ORdate_year2006 1.625e+01 1.139e+07 1.139e+04 0.001 0.999
ORdate_year2007 1.477e+01 2.601e+06 1.139e+04 0.001 0.999
ORdate_year2008 1.614e+01 1.026e+07 1.139e+04 0.001 0.999
ORdate_year2009 1.471e+01 2.454e+06 1.139e+04 0.001 0.999
ORdate_year2010 1.615e+01 1.035e+07 1.139e+04 0.001 0.999
ORdate_year2011 1.476e+01 2.572e+06 1.139e+04 0.001 0.999
ORdate_year2012 1.584e+01 7.549e+06 1.139e+04 0.001 0.999
ORdate_year2013 -5.063e-01 6.027e-01 1.247e+04 0.000 1.000
ORdate_year2014 -4.414e-01 6.432e-01 1.314e+04 0.000 1.000
ORdate_year2015 5.287e-02 1.054e+00 1.305e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.048e+00 9.544e-01 0.5617 1.954
Age 1.028e+00 9.731e-01 0.9925 1.064
Gendermale 1.520e+00 6.579e-01 0.7318 3.157
ORdate_year2002 1.306e+07 7.658e-08 0.0000 Inf
ORdate_year2003 4.550e+06 2.198e-07 0.0000 Inf
ORdate_year2004 5.436e+06 1.840e-07 0.0000 Inf
ORdate_year2005 9.139e+06 1.094e-07 0.0000 Inf
ORdate_year2006 1.139e+07 8.776e-08 0.0000 Inf
ORdate_year2007 2.601e+06 3.845e-07 0.0000 Inf
ORdate_year2008 1.026e+07 9.744e-08 0.0000 Inf
ORdate_year2009 2.454e+06 4.076e-07 0.0000 Inf
ORdate_year2010 1.035e+07 9.664e-08 0.0000 Inf
ORdate_year2011 2.572e+06 3.888e-07 0.0000 Inf
ORdate_year2012 7.549e+06 1.325e-07 0.0000 Inf
ORdate_year2013 6.027e-01 1.659e+00 0.0000 Inf
ORdate_year2014 6.432e-01 1.555e+00 0.0000 Inf
ORdate_year2015 1.054e+00 9.485e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.67 (se = 0.038 )
Likelihood ratio test= 18 on 17 df, p=0.4
Wald test = 11.96 on 17 df, p=0.8
Score (logrank) test = 16.29 on 17 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: CXCL10
Effect size...............: 0.046686
Standard error............: 0.318079
Odds ratio (effect size)..: 1.048
Lower 95% CI..............: 0.562
Upper 95% CI..............: 1.954
T-value...................: 0.146773
P-value...................: 0.883311
Sample size in model......: 619
Number of events..........: 47
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] -6.258e-01 5.348e-01 3.929e-01 -1.593 0.111
Age 2.500e-02 1.025e+00 1.781e-02 1.403 0.161
Gendermale 3.874e-01 1.473e+00 3.731e-01 1.038 0.299
ORdate_year2002 1.650e+01 1.468e+07 1.155e+04 0.001 0.999
ORdate_year2003 1.559e+01 5.878e+06 1.155e+04 0.001 0.999
ORdate_year2004 1.571e+01 6.651e+06 1.155e+04 0.001 0.999
ORdate_year2005 1.622e+01 1.104e+07 1.155e+04 0.001 0.999
ORdate_year2006 1.649e+01 1.455e+07 1.155e+04 0.001 0.999
ORdate_year2007 1.501e+01 3.312e+06 1.155e+04 0.001 0.999
ORdate_year2008 1.633e+01 1.241e+07 1.155e+04 0.001 0.999
ORdate_year2009 1.500e+01 3.265e+06 1.155e+04 0.001 0.999
ORdate_year2010 1.640e+01 1.319e+07 1.155e+04 0.001 0.999
ORdate_year2011 1.505e+01 3.436e+06 1.155e+04 0.001 0.999
ORdate_year2012 1.606e+01 9.424e+06 1.155e+04 0.001 0.999
ORdate_year2013 -2.261e-01 7.977e-01 1.258e+04 0.000 1.000
ORdate_year2014 -1.784e-01 8.366e-01 1.330e+04 0.000 1.000
ORdate_year2015 2.413e-01 1.273e+00 1.317e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 5.348e-01 1.870e+00 0.2476 1.155
Age 1.025e+00 9.753e-01 0.9901 1.062
Gendermale 1.473e+00 6.788e-01 0.7091 3.060
ORdate_year2002 1.468e+07 6.813e-08 0.0000 Inf
ORdate_year2003 5.878e+06 1.701e-07 0.0000 Inf
ORdate_year2004 6.651e+06 1.504e-07 0.0000 Inf
ORdate_year2005 1.104e+07 9.060e-08 0.0000 Inf
ORdate_year2006 1.455e+07 6.872e-08 0.0000 Inf
ORdate_year2007 3.312e+06 3.020e-07 0.0000 Inf
ORdate_year2008 1.241e+07 8.059e-08 0.0000 Inf
ORdate_year2009 3.265e+06 3.063e-07 0.0000 Inf
ORdate_year2010 1.319e+07 7.579e-08 0.0000 Inf
ORdate_year2011 3.436e+06 2.910e-07 0.0000 Inf
ORdate_year2012 9.424e+06 1.061e-07 0.0000 Inf
ORdate_year2013 7.977e-01 1.254e+00 0.0000 Inf
ORdate_year2014 8.366e-01 1.195e+00 0.0000 Inf
ORdate_year2015 1.273e+00 7.856e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.682 (se = 0.039 )
Likelihood ratio test= 20.85 on 17 df, p=0.2
Wald test = 14.38 on 17 df, p=0.6
Score (logrank) test = 18.93 on 17 df, p=0.3
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: PCSK9
Effect size...............: -0.62578
Standard error............: 0.392928
Odds ratio (effect size)..: 0.535
Lower 95% CI..............: 0.248
Upper 95% CI..............: 1.155
T-value...................: -1.592609
P-value...................: 0.111248
Sample size in model......: 619
Number of events..........: 47
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 7.103e-02 1.074e+00 2.984e-01 0.238 0.812
Age 2.701e-02 1.027e+00 1.767e-02 1.528 0.126
Gendermale 4.255e-01 1.530e+00 3.745e-01 1.136 0.256
ORdate_year2002 1.644e+01 1.383e+07 1.139e+04 0.001 0.999
ORdate_year2003 1.538e+01 4.788e+06 1.139e+04 0.001 0.999
ORdate_year2004 1.555e+01 5.676e+06 1.139e+04 0.001 0.999
ORdate_year2005 1.608e+01 9.582e+06 1.139e+04 0.001 0.999
ORdate_year2006 1.629e+01 1.193e+07 1.139e+04 0.001 0.999
ORdate_year2007 1.482e+01 2.720e+06 1.139e+04 0.001 0.999
ORdate_year2008 1.619e+01 1.073e+07 1.139e+04 0.001 0.999
ORdate_year2009 1.475e+01 2.539e+06 1.139e+04 0.001 0.999
ORdate_year2010 1.620e+01 1.082e+07 1.139e+04 0.001 0.999
ORdate_year2011 1.481e+01 2.707e+06 1.139e+04 0.001 0.999
ORdate_year2012 1.590e+01 8.020e+06 1.139e+04 0.001 0.999
ORdate_year2013 -4.756e-01 6.215e-01 1.246e+04 0.000 1.000
ORdate_year2014 -4.176e-01 6.587e-01 1.314e+04 0.000 1.000
ORdate_year2015 6.942e-02 1.072e+00 1.304e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.074e+00 9.314e-01 0.5983 1.927
Age 1.027e+00 9.734e-01 0.9924 1.064
Gendermale 1.530e+00 6.534e-01 0.7346 3.188
ORdate_year2002 1.383e+07 7.228e-08 0.0000 Inf
ORdate_year2003 4.788e+06 2.089e-07 0.0000 Inf
ORdate_year2004 5.676e+06 1.762e-07 0.0000 Inf
ORdate_year2005 9.582e+06 1.044e-07 0.0000 Inf
ORdate_year2006 1.193e+07 8.380e-08 0.0000 Inf
ORdate_year2007 2.720e+06 3.677e-07 0.0000 Inf
ORdate_year2008 1.073e+07 9.317e-08 0.0000 Inf
ORdate_year2009 2.539e+06 3.939e-07 0.0000 Inf
ORdate_year2010 1.082e+07 9.242e-08 0.0000 Inf
ORdate_year2011 2.707e+06 3.695e-07 0.0000 Inf
ORdate_year2012 8.020e+06 1.247e-07 0.0000 Inf
ORdate_year2013 6.215e-01 1.609e+00 0.0000 Inf
ORdate_year2014 6.587e-01 1.518e+00 0.0000 Inf
ORdate_year2015 1.072e+00 9.329e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.67 (se = 0.038 )
Likelihood ratio test= 18.04 on 17 df, p=0.4
Wald test = 11.98 on 17 df, p=0.8
Score (logrank) test = 16.31 on 17 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: COL4A1
Effect size...............: 0.071031
Standard error............: 0.298351
Odds ratio (effect size)..: 1.074
Lower 95% CI..............: 0.598
Upper 95% CI..............: 1.927
T-value...................: 0.238078
P-value...................: 0.8118207
Sample size in model......: 619
Number of events..........: 47
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.421e-01 1.153e+00 3.002e-01 0.473 0.636
Age 2.674e-02 1.027e+00 1.770e-02 1.511 0.131
Gendermale 4.359e-01 1.546e+00 3.752e-01 1.162 0.245
ORdate_year2002 1.647e+01 1.419e+07 1.140e+04 0.001 0.999
ORdate_year2003 1.541e+01 4.948e+06 1.140e+04 0.001 0.999
ORdate_year2004 1.557e+01 5.793e+06 1.140e+04 0.001 0.999
ORdate_year2005 1.609e+01 9.742e+06 1.140e+04 0.001 0.999
ORdate_year2006 1.632e+01 1.219e+07 1.140e+04 0.001 0.999
ORdate_year2007 1.487e+01 2.869e+06 1.140e+04 0.001 0.999
ORdate_year2008 1.623e+01 1.116e+07 1.140e+04 0.001 0.999
ORdate_year2009 1.477e+01 2.587e+06 1.140e+04 0.001 0.999
ORdate_year2010 1.622e+01 1.104e+07 1.140e+04 0.001 0.999
ORdate_year2011 1.483e+01 2.770e+06 1.140e+04 0.001 0.999
ORdate_year2012 1.594e+01 8.328e+06 1.140e+04 0.001 0.999
ORdate_year2013 -4.262e-01 6.530e-01 1.248e+04 0.000 1.000
ORdate_year2014 -4.280e-01 6.518e-01 1.315e+04 0.000 1.000
ORdate_year2015 8.731e-02 1.091e+00 1.303e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.153e+00 8.676e-01 0.6400 2.076
Age 1.027e+00 9.736e-01 0.9921 1.063
Gendermale 1.546e+00 6.467e-01 0.7412 3.226
ORdate_year2002 1.419e+07 7.047e-08 0.0000 Inf
ORdate_year2003 4.948e+06 2.021e-07 0.0000 Inf
ORdate_year2004 5.793e+06 1.726e-07 0.0000 Inf
ORdate_year2005 9.742e+06 1.027e-07 0.0000 Inf
ORdate_year2006 1.219e+07 8.200e-08 0.0000 Inf
ORdate_year2007 2.869e+06 3.485e-07 0.0000 Inf
ORdate_year2008 1.116e+07 8.958e-08 0.0000 Inf
ORdate_year2009 2.587e+06 3.865e-07 0.0000 Inf
ORdate_year2010 1.104e+07 9.062e-08 0.0000 Inf
ORdate_year2011 2.770e+06 3.610e-07 0.0000 Inf
ORdate_year2012 8.328e+06 1.201e-07 0.0000 Inf
ORdate_year2013 6.530e-01 1.531e+00 0.0000 Inf
ORdate_year2014 6.518e-01 1.534e+00 0.0000 Inf
ORdate_year2015 1.091e+00 9.164e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.672 (se = 0.038 )
Likelihood ratio test= 18.21 on 17 df, p=0.4
Wald test = 12.13 on 17 df, p=0.8
Score (logrank) test = 16.46 on 17 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: COL4A2
Effect size...............: 0.14207
Standard error............: 0.300189
Odds ratio (effect size)..: 1.153
Lower 95% CI..............: 0.64
Upper 95% CI..............: 2.076
T-value...................: 0.473268
P-value...................: 0.6360217
Sample size in model......: 619
Number of events..........: 47
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -3.658e-01 6.936e-01 3.039e-01 -1.203 0.229
Age 2.764e-02 1.028e+00 1.784e-02 1.549 0.121
Gendermale 4.034e-01 1.497e+00 3.734e-01 1.080 0.280
ORdate_year2002 1.650e+01 1.464e+07 1.149e+04 0.001 0.999
ORdate_year2003 1.553e+01 5.538e+06 1.149e+04 0.001 0.999
ORdate_year2004 1.569e+01 6.518e+06 1.149e+04 0.001 0.999
ORdate_year2005 1.625e+01 1.140e+07 1.149e+04 0.001 0.999
ORdate_year2006 1.648e+01 1.440e+07 1.149e+04 0.001 0.999
ORdate_year2007 1.499e+01 3.244e+06 1.149e+04 0.001 0.999
ORdate_year2008 1.635e+01 1.257e+07 1.149e+04 0.001 0.999
ORdate_year2009 1.500e+01 3.278e+06 1.149e+04 0.001 0.999
ORdate_year2010 1.638e+01 1.304e+07 1.149e+04 0.001 0.999
ORdate_year2011 1.496e+01 3.153e+06 1.149e+04 0.001 0.999
ORdate_year2012 1.597e+01 8.660e+06 1.149e+04 0.001 0.999
ORdate_year2013 -2.628e-01 7.689e-01 1.258e+04 0.000 1.000
ORdate_year2014 -1.501e-01 8.606e-01 1.321e+04 0.000 1.000
ORdate_year2015 3.065e-01 1.359e+00 1.322e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 6.936e-01 1.442e+00 0.3823 1.259
Age 1.028e+00 9.727e-01 0.9927 1.065
Gendermale 1.497e+00 6.680e-01 0.7201 3.112
ORdate_year2002 1.464e+07 6.829e-08 0.0000 Inf
ORdate_year2003 5.538e+06 1.806e-07 0.0000 Inf
ORdate_year2004 6.518e+06 1.534e-07 0.0000 Inf
ORdate_year2005 1.140e+07 8.774e-08 0.0000 Inf
ORdate_year2006 1.440e+07 6.945e-08 0.0000 Inf
ORdate_year2007 3.244e+06 3.083e-07 0.0000 Inf
ORdate_year2008 1.257e+07 7.958e-08 0.0000 Inf
ORdate_year2009 3.278e+06 3.051e-07 0.0000 Inf
ORdate_year2010 1.304e+07 7.671e-08 0.0000 Inf
ORdate_year2011 3.153e+06 3.172e-07 0.0000 Inf
ORdate_year2012 8.660e+06 1.155e-07 0.0000 Inf
ORdate_year2013 7.689e-01 1.301e+00 0.0000 Inf
ORdate_year2014 8.606e-01 1.162e+00 0.0000 Inf
ORdate_year2015 1.359e+00 7.360e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.674 (se = 0.036 )
Likelihood ratio test= 19.45 on 17 df, p=0.3
Wald test = 13.37 on 17 df, p=0.7
Score (logrank) test = 17.86 on 17 df, p=0.4
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: LDLR
Effect size...............: -0.365801
Standard error............: 0.303953
Odds ratio (effect size)..: 0.694
Lower 95% CI..............: 0.382
Upper 95% CI..............: 1.259
T-value...................: -1.20348
P-value...................: 0.2287908
Sample size in model......: 619
Number of events..........: 47
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 47
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 3.814e-01 1.464e+00 3.022e-01 1.262 0.207
Age 2.663e-02 1.027e+00 1.740e-02 1.530 0.126
Gendermale 4.466e-01 1.563e+00 3.743e-01 1.193 0.233
ORdate_year2002 1.667e+01 1.734e+07 1.148e+04 0.001 0.999
ORdate_year2003 1.556e+01 5.711e+06 1.148e+04 0.001 0.999
ORdate_year2004 1.570e+01 6.573e+06 1.148e+04 0.001 0.999
ORdate_year2005 1.620e+01 1.084e+07 1.148e+04 0.001 0.999
ORdate_year2006 1.642e+01 1.347e+07 1.148e+04 0.001 0.999
ORdate_year2007 1.497e+01 3.170e+06 1.148e+04 0.001 0.999
ORdate_year2008 1.635e+01 1.262e+07 1.148e+04 0.001 0.999
ORdate_year2009 1.482e+01 2.742e+06 1.148e+04 0.001 0.999
ORdate_year2010 1.632e+01 1.223e+07 1.148e+04 0.001 0.999
ORdate_year2011 1.495e+01 3.101e+06 1.148e+04 0.001 0.999
ORdate_year2012 1.603e+01 9.140e+06 1.148e+04 0.001 0.999
ORdate_year2013 -1.413e-01 8.682e-01 1.257e+04 0.000 1.000
ORdate_year2014 -4.387e-01 6.449e-01 1.324e+04 0.000 1.000
ORdate_year2015 1.626e-01 1.177e+00 1.306e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.464e+00 6.829e-01 0.8098 2.648
Age 1.027e+00 9.737e-01 0.9926 1.063
Gendermale 1.563e+00 6.398e-01 0.7505 3.255
ORdate_year2002 1.734e+07 5.767e-08 0.0000 Inf
ORdate_year2003 5.711e+06 1.751e-07 0.0000 Inf
ORdate_year2004 6.573e+06 1.521e-07 0.0000 Inf
ORdate_year2005 1.084e+07 9.222e-08 0.0000 Inf
ORdate_year2006 1.347e+07 7.424e-08 0.0000 Inf
ORdate_year2007 3.170e+06 3.154e-07 0.0000 Inf
ORdate_year2008 1.262e+07 7.924e-08 0.0000 Inf
ORdate_year2009 2.742e+06 3.646e-07 0.0000 Inf
ORdate_year2010 1.223e+07 8.174e-08 0.0000 Inf
ORdate_year2011 3.101e+06 3.225e-07 0.0000 Inf
ORdate_year2012 9.140e+06 1.094e-07 0.0000 Inf
ORdate_year2013 8.682e-01 1.152e+00 0.0000 Inf
ORdate_year2014 6.449e-01 1.551e+00 0.0000 Inf
ORdate_year2015 1.177e+00 8.499e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.676 (se = 0.04 )
Likelihood ratio test= 19.6 on 17 df, p=0.3
Wald test = 13.49 on 17 df, p=0.7
Score (logrank) test = 17.84 on 17 df, p=0.4
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: CD36
Effect size...............: 0.381353
Standard error............: 0.302213
Odds ratio (effect size)..: 1.464
Lower 95% CI..............: 0.81
Upper 95% CI..............: 2.648
T-value...................: 1.261872
P-value...................: 0.206995
Sample size in model......: 619
Number of events..........: 47
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 3.845e-01 1.469e+00 3.042e-01 1.264 0.206
Age 3.278e-03 1.003e+00 1.719e-02 0.191 0.849
Gendermale 9.643e-01 2.623e+00 4.389e-01 2.197 0.028 *
ORdate_year2002 1.616e+01 1.038e+07 1.121e+04 0.001 0.999
ORdate_year2003 1.553e+01 5.548e+06 1.121e+04 0.001 0.999
ORdate_year2004 1.463e+01 2.262e+06 1.121e+04 0.001 0.999
ORdate_year2005 1.538e+01 4.786e+06 1.121e+04 0.001 0.999
ORdate_year2006 1.569e+01 6.499e+06 1.121e+04 0.001 0.999
ORdate_year2007 1.526e+01 4.249e+06 1.121e+04 0.001 0.999
ORdate_year2008 1.537e+01 4.715e+06 1.121e+04 0.001 0.999
ORdate_year2009 1.492e+01 3.018e+06 1.121e+04 0.001 0.999
ORdate_year2010 1.421e+01 1.478e+06 1.121e+04 0.001 0.999
ORdate_year2011 1.433e+01 1.664e+06 1.121e+04 0.001 0.999
ORdate_year2012 1.457e+01 2.137e+06 1.121e+04 0.001 0.999
ORdate_year2013 -1.048e+00 3.505e-01 1.227e+04 0.000 1.000
ORdate_year2014 -1.220e+00 2.953e-01 1.292e+04 0.000 1.000
ORdate_year2015 -3.353e-01 7.151e-01 1.282e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.469e+00 6.808e-01 0.8092 2.667
Age 1.003e+00 9.967e-01 0.9701 1.038
Gendermale 2.623e+00 3.812e-01 1.1096 6.201
ORdate_year2002 1.038e+07 9.635e-08 0.0000 Inf
ORdate_year2003 5.548e+06 1.802e-07 0.0000 Inf
ORdate_year2004 2.262e+06 4.421e-07 0.0000 Inf
ORdate_year2005 4.786e+06 2.089e-07 0.0000 Inf
ORdate_year2006 6.499e+06 1.539e-07 0.0000 Inf
ORdate_year2007 4.249e+06 2.353e-07 0.0000 Inf
ORdate_year2008 4.715e+06 2.121e-07 0.0000 Inf
ORdate_year2009 3.018e+06 3.313e-07 0.0000 Inf
ORdate_year2010 1.478e+06 6.767e-07 0.0000 Inf
ORdate_year2011 1.664e+06 6.008e-07 0.0000 Inf
ORdate_year2012 2.137e+06 4.680e-07 0.0000 Inf
ORdate_year2013 3.505e-01 2.853e+00 0.0000 Inf
ORdate_year2014 2.953e-01 3.386e+00 0.0000 Inf
ORdate_year2015 7.151e-01 1.398e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.673 (se = 0.037 )
Likelihood ratio test= 18.16 on 17 df, p=0.4
Wald test = 14.42 on 17 df, p=0.6
Score (logrank) test = 16.67 on 17 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: CXCL10
Effect size...............: 0.384538
Standard error............: 0.304232
Odds ratio (effect size)..: 1.469
Lower 95% CI..............: 0.809
Upper 95% CI..............: 2.667
T-value...................: 1.263964
P-value...................: 0.206243
Sample size in model......: 619
Number of events..........: 48
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] -1.862e-01 8.301e-01 3.392e-01 -0.549 0.5832
Age 1.491e-03 1.001e+00 1.720e-02 0.087 0.9309
Gendermale 9.419e-01 2.565e+00 4.386e-01 2.148 0.0317 *
ORdate_year2002 1.628e+01 1.171e+07 1.115e+04 0.001 0.9988
ORdate_year2003 1.570e+01 6.552e+06 1.115e+04 0.001 0.9989
ORdate_year2004 1.485e+01 2.823e+06 1.115e+04 0.001 0.9989
ORdate_year2005 1.561e+01 6.016e+06 1.115e+04 0.001 0.9989
ORdate_year2006 1.589e+01 7.985e+06 1.115e+04 0.001 0.9989
ORdate_year2007 1.543e+01 5.029e+06 1.115e+04 0.001 0.9989
ORdate_year2008 1.551e+01 5.457e+06 1.115e+04 0.001 0.9989
ORdate_year2009 1.517e+01 3.884e+06 1.115e+04 0.001 0.9989
ORdate_year2010 1.442e+01 1.836e+06 1.115e+04 0.001 0.9990
ORdate_year2011 1.454e+01 2.065e+06 1.115e+04 0.001 0.9990
ORdate_year2012 1.480e+01 2.663e+06 1.115e+04 0.001 0.9989
ORdate_year2013 -8.712e-01 4.184e-01 1.222e+04 0.000 0.9999
ORdate_year2014 -8.797e-01 4.149e-01 1.287e+04 0.000 0.9999
ORdate_year2015 -2.800e-01 7.558e-01 1.273e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 8.301e-01 1.205e+00 0.4270 1.614
Age 1.001e+00 9.985e-01 0.9683 1.036
Gendermale 2.565e+00 3.899e-01 1.0858 6.058
ORdate_year2002 1.171e+07 8.540e-08 0.0000 Inf
ORdate_year2003 6.552e+06 1.526e-07 0.0000 Inf
ORdate_year2004 2.823e+06 3.542e-07 0.0000 Inf
ORdate_year2005 6.016e+06 1.662e-07 0.0000 Inf
ORdate_year2006 7.985e+06 1.252e-07 0.0000 Inf
ORdate_year2007 5.029e+06 1.988e-07 0.0000 Inf
ORdate_year2008 5.457e+06 1.833e-07 0.0000 Inf
ORdate_year2009 3.884e+06 2.575e-07 0.0000 Inf
ORdate_year2010 1.836e+06 5.446e-07 0.0000 Inf
ORdate_year2011 2.065e+06 4.841e-07 0.0000 Inf
ORdate_year2012 2.663e+06 3.755e-07 0.0000 Inf
ORdate_year2013 4.184e-01 2.390e+00 0.0000 Inf
ORdate_year2014 4.149e-01 2.410e+00 0.0000 Inf
ORdate_year2015 7.558e-01 1.323e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.658 (se = 0.036 )
Likelihood ratio test= 16.92 on 17 df, p=0.5
Wald test = 13.25 on 17 df, p=0.7
Score (logrank) test = 15.51 on 17 df, p=0.6
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: PCSK9
Effect size...............: -0.18616
Standard error............: 0.339224
Odds ratio (effect size)..: 0.83
Lower 95% CI..............: 0.427
Upper 95% CI..............: 1.614
T-value...................: -0.548781
P-value...................: 0.5831557
Sample size in model......: 619
Number of events..........: 48
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] -1.756e-01 8.389e-01 3.015e-01 -0.583 0.5602
Age 2.200e-03 1.002e+00 1.724e-02 0.128 0.8984
Gendermale 9.291e-01 2.532e+00 4.395e-01 2.114 0.0345 *
ORdate_year2002 1.614e+01 1.024e+07 1.116e+04 0.001 0.9988
ORdate_year2003 1.552e+01 5.525e+06 1.116e+04 0.001 0.9989
ORdate_year2004 1.474e+01 2.522e+06 1.116e+04 0.001 0.9989
ORdate_year2005 1.549e+01 5.347e+06 1.116e+04 0.001 0.9989
ORdate_year2006 1.576e+01 6.980e+06 1.116e+04 0.001 0.9989
ORdate_year2007 1.529e+01 4.355e+06 1.116e+04 0.001 0.9989
ORdate_year2008 1.537e+01 4.736e+06 1.116e+04 0.001 0.9989
ORdate_year2009 1.506e+01 3.469e+06 1.116e+04 0.001 0.9989
ORdate_year2010 1.430e+01 1.622e+06 1.116e+04 0.001 0.9990
ORdate_year2011 1.438e+01 1.754e+06 1.116e+04 0.001 0.9990
ORdate_year2012 1.464e+01 2.288e+06 1.116e+04 0.001 0.9990
ORdate_year2013 -1.005e+00 3.660e-01 1.224e+04 0.000 0.9999
ORdate_year2014 -9.282e-01 3.953e-01 1.289e+04 0.000 0.9999
ORdate_year2015 -3.829e-01 6.819e-01 1.281e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 8.389e-01 1.192e+00 0.4646 1.515
Age 1.002e+00 9.978e-01 0.9689 1.037
Gendermale 2.532e+00 3.949e-01 1.0700 5.992
ORdate_year2002 1.024e+07 9.762e-08 0.0000 Inf
ORdate_year2003 5.525e+06 1.810e-07 0.0000 Inf
ORdate_year2004 2.522e+06 3.965e-07 0.0000 Inf
ORdate_year2005 5.347e+06 1.870e-07 0.0000 Inf
ORdate_year2006 6.980e+06 1.433e-07 0.0000 Inf
ORdate_year2007 4.355e+06 2.296e-07 0.0000 Inf
ORdate_year2008 4.736e+06 2.111e-07 0.0000 Inf
ORdate_year2009 3.469e+06 2.882e-07 0.0000 Inf
ORdate_year2010 1.622e+06 6.165e-07 0.0000 Inf
ORdate_year2011 1.754e+06 5.702e-07 0.0000 Inf
ORdate_year2012 2.288e+06 4.371e-07 0.0000 Inf
ORdate_year2013 3.660e-01 2.733e+00 0.0000 Inf
ORdate_year2014 3.953e-01 2.530e+00 0.0000 Inf
ORdate_year2015 6.819e-01 1.467e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.652 (se = 0.036 )
Likelihood ratio test= 16.96 on 17 df, p=0.5
Wald test = 13.35 on 17 df, p=0.7
Score (logrank) test = 15.66 on 17 df, p=0.5
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: COL4A1
Effect size...............: -0.175631
Standard error............: 0.301505
Odds ratio (effect size)..: 0.839
Lower 95% CI..............: 0.465
Upper 95% CI..............: 1.515
T-value...................: -0.582516
P-value...................: 0.5602189
Sample size in model......: 619
Number of events..........: 48
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.587e-01 1.172e+00 3.008e-01 0.528 0.5978
Age 1.915e-03 1.002e+00 1.715e-02 0.112 0.9111
Gendermale 9.764e-01 2.655e+00 4.422e-01 2.208 0.0272 *
ORdate_year2002 1.633e+01 1.236e+07 1.113e+04 0.001 0.9988
ORdate_year2003 1.569e+01 6.544e+06 1.113e+04 0.001 0.9989
ORdate_year2004 1.483e+01 2.771e+06 1.113e+04 0.001 0.9989
ORdate_year2005 1.560e+01 5.958e+06 1.113e+04 0.001 0.9989
ORdate_year2006 1.587e+01 7.811e+06 1.113e+04 0.001 0.9989
ORdate_year2007 1.545e+01 5.144e+06 1.113e+04 0.001 0.9989
ORdate_year2008 1.554e+01 5.607e+06 1.113e+04 0.001 0.9989
ORdate_year2009 1.511e+01 3.666e+06 1.113e+04 0.001 0.9989
ORdate_year2010 1.440e+01 1.802e+06 1.113e+04 0.001 0.9990
ORdate_year2011 1.451e+01 2.007e+06 1.113e+04 0.001 0.9990
ORdate_year2012 1.481e+01 2.694e+06 1.113e+04 0.001 0.9989
ORdate_year2013 -8.861e-01 4.123e-01 1.220e+04 0.000 0.9999
ORdate_year2014 -9.757e-01 3.769e-01 1.285e+04 0.000 0.9999
ORdate_year2015 -3.045e-01 7.375e-01 1.271e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.172e+00 8.533e-01 0.6500 2.113
Age 1.002e+00 9.981e-01 0.9688 1.036
Gendermale 2.655e+00 3.767e-01 1.1160 6.316
ORdate_year2002 1.236e+07 8.092e-08 0.0000 Inf
ORdate_year2003 6.544e+06 1.528e-07 0.0000 Inf
ORdate_year2004 2.771e+06 3.609e-07 0.0000 Inf
ORdate_year2005 5.958e+06 1.678e-07 0.0000 Inf
ORdate_year2006 7.811e+06 1.280e-07 0.0000 Inf
ORdate_year2007 5.144e+06 1.944e-07 0.0000 Inf
ORdate_year2008 5.607e+06 1.783e-07 0.0000 Inf
ORdate_year2009 3.666e+06 2.728e-07 0.0000 Inf
ORdate_year2010 1.802e+06 5.549e-07 0.0000 Inf
ORdate_year2011 2.007e+06 4.983e-07 0.0000 Inf
ORdate_year2012 2.694e+06 3.712e-07 0.0000 Inf
ORdate_year2013 4.123e-01 2.426e+00 0.0000 Inf
ORdate_year2014 3.769e-01 2.653e+00 0.0000 Inf
ORdate_year2015 7.375e-01 1.356e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.661 (se = 0.037 )
Likelihood ratio test= 16.89 on 17 df, p=0.5
Wald test = 13.08 on 17 df, p=0.7
Score (logrank) test = 15.34 on 17 df, p=0.6
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: COL4A2
Effect size...............: 0.15869
Standard error............: 0.30078
Odds ratio (effect size)..: 1.172
Lower 95% CI..............: 0.65
Upper 95% CI..............: 2.113
T-value...................: 0.527595
P-value...................: 0.5977803
Sample size in model......: 619
Number of events..........: 48
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -4.236e-01 6.547e-01 3.020e-01 -1.402 0.1608
Age 2.445e-03 1.002e+00 1.725e-02 0.142 0.8873
Gendermale 9.422e-01 2.566e+00 4.387e-01 2.148 0.0317 *
ORdate_year2002 1.638e+01 1.303e+07 1.123e+04 0.001 0.9988
ORdate_year2003 1.581e+01 7.337e+06 1.123e+04 0.001 0.9989
ORdate_year2004 1.497e+01 3.187e+06 1.123e+04 0.001 0.9989
ORdate_year2005 1.579e+01 7.176e+06 1.123e+04 0.001 0.9989
ORdate_year2006 1.608e+01 9.590e+06 1.123e+04 0.001 0.9989
ORdate_year2007 1.559e+01 5.892e+06 1.123e+04 0.001 0.9989
ORdate_year2008 1.565e+01 6.257e+06 1.123e+04 0.001 0.9989
ORdate_year2009 1.539e+01 4.815e+06 1.123e+04 0.001 0.9989
ORdate_year2010 1.458e+01 2.155e+06 1.123e+04 0.001 0.9990
ORdate_year2011 1.466e+01 2.332e+06 1.123e+04 0.001 0.9990
ORdate_year2012 1.485e+01 2.801e+06 1.123e+04 0.001 0.9989
ORdate_year2013 -6.992e-01 4.970e-01 1.230e+04 0.000 1.0000
ORdate_year2014 -6.593e-01 5.172e-01 1.294e+04 0.000 1.0000
ORdate_year2015 -4.107e-02 9.598e-01 1.290e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 6.547e-01 1.527e+00 0.3622 1.183
Age 1.002e+00 9.976e-01 0.9691 1.037
Gendermale 2.566e+00 3.898e-01 1.0858 6.062
ORdate_year2002 1.303e+07 7.677e-08 0.0000 Inf
ORdate_year2003 7.337e+06 1.363e-07 0.0000 Inf
ORdate_year2004 3.187e+06 3.137e-07 0.0000 Inf
ORdate_year2005 7.176e+06 1.394e-07 0.0000 Inf
ORdate_year2006 9.590e+06 1.043e-07 0.0000 Inf
ORdate_year2007 5.892e+06 1.697e-07 0.0000 Inf
ORdate_year2008 6.257e+06 1.598e-07 0.0000 Inf
ORdate_year2009 4.815e+06 2.077e-07 0.0000 Inf
ORdate_year2010 2.155e+06 4.640e-07 0.0000 Inf
ORdate_year2011 2.332e+06 4.288e-07 0.0000 Inf
ORdate_year2012 2.801e+06 3.570e-07 0.0000 Inf
ORdate_year2013 4.970e-01 2.012e+00 0.0000 Inf
ORdate_year2014 5.172e-01 1.933e+00 0.0000 Inf
ORdate_year2015 9.598e-01 1.042e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.664 (se = 0.036 )
Likelihood ratio test= 18.62 on 17 df, p=0.4
Wald test = 14.8 on 17 df, p=0.6
Score (logrank) test = 17.11 on 17 df, p=0.4
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: LDLR
Effect size...............: -0.423587
Standard error............: 0.302034
Odds ratio (effect size)..: 0.655
Lower 95% CI..............: 0.362
Upper 95% CI..............: 1.183
T-value...................: -1.402447
P-value...................: 0.1607819
Sample size in model......: 619
Number of events..........: 48
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 48
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] -1.762e-01 8.384e-01 2.994e-01 -0.588 0.5562
Age 1.872e-03 1.002e+00 1.728e-02 0.108 0.9137
Gendermale 9.392e-01 2.558e+00 4.387e-01 2.141 0.0323 *
ORdate_year2002 1.613e+01 1.014e+07 1.115e+04 0.001 0.9988
ORdate_year2003 1.552e+01 5.503e+06 1.115e+04 0.001 0.9989
ORdate_year2004 1.472e+01 2.459e+06 1.115e+04 0.001 0.9989
ORdate_year2005 1.549e+01 5.334e+06 1.115e+04 0.001 0.9989
ORdate_year2006 1.576e+01 6.963e+06 1.115e+04 0.001 0.9989
ORdate_year2007 1.528e+01 4.305e+06 1.115e+04 0.001 0.9989
ORdate_year2008 1.536e+01 4.700e+06 1.115e+04 0.001 0.9989
ORdate_year2009 1.505e+01 3.448e+06 1.115e+04 0.001 0.9989
ORdate_year2010 1.429e+01 1.602e+06 1.115e+04 0.001 0.9990
ORdate_year2011 1.437e+01 1.741e+06 1.115e+04 0.001 0.9990
ORdate_year2012 1.465e+01 2.306e+06 1.115e+04 0.001 0.9990
ORdate_year2013 -1.120e+00 3.263e-01 1.223e+04 0.000 0.9999
ORdate_year2014 -9.385e-01 3.912e-01 1.288e+04 0.000 0.9999
ORdate_year2015 -3.888e-01 6.779e-01 1.279e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 8.384e-01 1.193e+00 0.4662 1.508
Age 1.002e+00 9.981e-01 0.9685 1.036
Gendermale 2.558e+00 3.909e-01 1.0826 6.044
ORdate_year2002 1.014e+07 9.864e-08 0.0000 Inf
ORdate_year2003 5.503e+06 1.817e-07 0.0000 Inf
ORdate_year2004 2.459e+06 4.066e-07 0.0000 Inf
ORdate_year2005 5.334e+06 1.875e-07 0.0000 Inf
ORdate_year2006 6.963e+06 1.436e-07 0.0000 Inf
ORdate_year2007 4.305e+06 2.323e-07 0.0000 Inf
ORdate_year2008 4.700e+06 2.128e-07 0.0000 Inf
ORdate_year2009 3.448e+06 2.901e-07 0.0000 Inf
ORdate_year2010 1.602e+06 6.242e-07 0.0000 Inf
ORdate_year2011 1.741e+06 5.743e-07 0.0000 Inf
ORdate_year2012 2.306e+06 4.336e-07 0.0000 Inf
ORdate_year2013 3.263e-01 3.065e+00 0.0000 Inf
ORdate_year2014 3.912e-01 2.556e+00 0.0000 Inf
ORdate_year2015 6.779e-01 1.475e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.653 (se = 0.036 )
Likelihood ratio test= 16.96 on 17 df, p=0.5
Wald test = 13.29 on 17 df, p=0.7
Score (logrank) test = 15.58 on 17 df, p=0.6
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: CD36
Effect size...............: -0.176202
Standard error............: 0.29944
Odds ratio (effect size)..: 0.838
Lower 95% CI..............: 0.466
Upper 95% CI..............: 1.508
T-value...................: -0.588438
P-value...................: 0.5562387
Sample size in model......: 619
Number of events..........: 48
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 3.391e-01 1.404e+00 4.052e-01 0.837 0.40262
Age 8.194e-02 1.085e+00 2.616e-02 3.132 0.00173 **
Gendermale 7.971e-01 2.219e+00 5.415e-01 1.472 0.14102
ORdate_year2002 1.812e+01 7.422e+07 3.332e+04 0.001 0.99957
ORdate_year2003 1.637e+01 1.293e+07 3.332e+04 0.000 0.99961
ORdate_year2004 1.624e+01 1.126e+07 3.332e+04 0.000 0.99961
ORdate_year2005 1.701e+01 2.437e+07 3.332e+04 0.001 0.99959
ORdate_year2006 1.597e+01 8.627e+06 3.332e+04 0.000 0.99962
ORdate_year2007 1.661e+01 1.631e+07 3.332e+04 0.000 0.99960
ORdate_year2008 1.675e+01 1.877e+07 3.332e+04 0.001 0.99960
ORdate_year2009 1.674e+01 1.870e+07 3.332e+04 0.001 0.99960
ORdate_year2010 1.682e+01 2.016e+07 3.332e+04 0.001 0.99960
ORdate_year2011 -1.247e+00 2.875e-01 3.374e+04 0.000 0.99997
ORdate_year2012 -1.147e+00 3.175e-01 3.390e+04 0.000 0.99997
ORdate_year2013 -1.164e+00 3.121e-01 3.602e+04 0.000 0.99997
ORdate_year2014 -1.016e+00 3.619e-01 3.825e+04 0.000 0.99998
ORdate_year2015 1.914e-01 1.211e+00 3.709e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.404e+00 7.124e-01 0.6344 3.106
Age 1.085e+00 9.213e-01 1.0311 1.142
Gendermale 2.219e+00 4.506e-01 0.7678 6.413
ORdate_year2002 7.422e+07 1.347e-08 0.0000 Inf
ORdate_year2003 1.293e+07 7.736e-08 0.0000 Inf
ORdate_year2004 1.126e+07 8.879e-08 0.0000 Inf
ORdate_year2005 2.437e+07 4.104e-08 0.0000 Inf
ORdate_year2006 8.627e+06 1.159e-07 0.0000 Inf
ORdate_year2007 1.631e+07 6.131e-08 0.0000 Inf
ORdate_year2008 1.877e+07 5.328e-08 0.0000 Inf
ORdate_year2009 1.870e+07 5.347e-08 0.0000 Inf
ORdate_year2010 2.016e+07 4.960e-08 0.0000 Inf
ORdate_year2011 2.875e-01 3.478e+00 0.0000 Inf
ORdate_year2012 3.175e-01 3.150e+00 0.0000 Inf
ORdate_year2013 3.121e-01 3.204e+00 0.0000 Inf
ORdate_year2014 3.619e-01 2.763e+00 0.0000 Inf
ORdate_year2015 1.211e+00 8.258e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.774 (se = 0.036 )
Likelihood ratio test= 28.33 on 17 df, p=0.04
Wald test = 12.94 on 17 df, p=0.7
Score (logrank) test = 26.93 on 17 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: CXCL10
Effect size...............: 0.339127
Standard error............: 0.405191
Odds ratio (effect size)..: 1.404
Lower 95% CI..............: 0.634
Upper 95% CI..............: 3.106
T-value...................: 0.836956
P-value...................: 0.4026172
Sample size in model......: 619
Number of events..........: 28
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 4.207e-01 1.523e+00 4.265e-01 0.986 0.32399
Age 8.165e-02 1.085e+00 2.579e-02 3.166 0.00154 **
Gendermale 7.985e-01 2.222e+00 5.419e-01 1.474 0.14061
ORdate_year2002 1.817e+01 7.762e+07 3.309e+04 0.001 0.99956
ORdate_year2003 1.626e+01 1.154e+07 3.309e+04 0.000 0.99961
ORdate_year2004 1.624e+01 1.135e+07 3.309e+04 0.000 0.99961
ORdate_year2005 1.703e+01 2.499e+07 3.309e+04 0.001 0.99959
ORdate_year2006 1.594e+01 8.350e+06 3.309e+04 0.000 0.99962
ORdate_year2007 1.651e+01 1.481e+07 3.309e+04 0.000 0.99960
ORdate_year2008 1.669e+01 1.776e+07 3.309e+04 0.001 0.99960
ORdate_year2009 1.670e+01 1.782e+07 3.309e+04 0.001 0.99960
ORdate_year2010 1.679e+01 1.966e+07 3.309e+04 0.001 0.99960
ORdate_year2011 -1.319e+00 2.673e-01 3.350e+04 0.000 0.99997
ORdate_year2012 -1.132e+00 3.223e-01 3.368e+04 0.000 0.99997
ORdate_year2013 -1.248e+00 2.870e-01 3.588e+04 0.000 0.99997
ORdate_year2014 -9.543e-01 3.851e-01 3.771e+04 0.000 0.99998
ORdate_year2015 7.135e-02 1.074e+00 3.694e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 1.523e+00 6.566e-01 0.6601 3.514
Age 1.085e+00 9.216e-01 1.0316 1.141
Gendermale 2.222e+00 4.500e-01 0.7683 6.428
ORdate_year2002 7.762e+07 1.288e-08 0.0000 Inf
ORdate_year2003 1.154e+07 8.663e-08 0.0000 Inf
ORdate_year2004 1.135e+07 8.810e-08 0.0000 Inf
ORdate_year2005 2.499e+07 4.001e-08 0.0000 Inf
ORdate_year2006 8.350e+06 1.198e-07 0.0000 Inf
ORdate_year2007 1.481e+07 6.753e-08 0.0000 Inf
ORdate_year2008 1.776e+07 5.631e-08 0.0000 Inf
ORdate_year2009 1.782e+07 5.612e-08 0.0000 Inf
ORdate_year2010 1.966e+07 5.088e-08 0.0000 Inf
ORdate_year2011 2.673e-01 3.741e+00 0.0000 Inf
ORdate_year2012 3.223e-01 3.103e+00 0.0000 Inf
ORdate_year2013 2.870e-01 3.484e+00 0.0000 Inf
ORdate_year2014 3.851e-01 2.597e+00 0.0000 Inf
ORdate_year2015 1.074e+00 9.311e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.775 (se = 0.035 )
Likelihood ratio test= 28.59 on 17 df, p=0.04
Wald test = 13.04 on 17 df, p=0.7
Score (logrank) test = 27.09 on 17 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: PCSK9
Effect size...............: 0.420706
Standard error............: 0.426553
Odds ratio (effect size)..: 1.523
Lower 95% CI..............: 0.66
Upper 95% CI..............: 3.514
T-value...................: 0.986292
P-value...................: 0.3239897
Sample size in model......: 619
Number of events..........: 28
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.353e-01 1.145e+00 4.029e-01 0.336 0.73705
Age 7.968e-02 1.083e+00 2.571e-02 3.099 0.00194 **
Gendermale 8.148e-01 2.259e+00 5.472e-01 1.489 0.13651
ORdate_year2002 1.830e+01 8.820e+07 3.319e+04 0.001 0.99956
ORdate_year2003 1.653e+01 1.506e+07 3.319e+04 0.000 0.99960
ORdate_year2004 1.643e+01 1.364e+07 3.319e+04 0.000 0.99961
ORdate_year2005 1.721e+01 2.984e+07 3.319e+04 0.001 0.99959
ORdate_year2006 1.615e+01 1.029e+07 3.319e+04 0.000 0.99961
ORdate_year2007 1.677e+01 1.913e+07 3.319e+04 0.001 0.99960
ORdate_year2008 1.688e+01 2.135e+07 3.319e+04 0.001 0.99959
ORdate_year2009 1.691e+01 2.216e+07 3.319e+04 0.001 0.99959
ORdate_year2010 1.700e+01 2.415e+07 3.319e+04 0.001 0.99959
ORdate_year2011 -1.060e+00 3.463e-01 3.361e+04 0.000 0.99997
ORdate_year2012 -9.232e-01 3.973e-01 3.379e+04 0.000 0.99998
ORdate_year2013 -1.060e+00 3.464e-01 3.588e+04 0.000 0.99998
ORdate_year2014 -8.115e-01 4.442e-01 3.805e+04 0.000 0.99998
ORdate_year2015 1.946e-01 1.215e+00 3.691e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.145e+00 8.735e-01 0.5198 2.522
Age 1.083e+00 9.234e-01 1.0297 1.139
Gendermale 2.259e+00 4.427e-01 0.7728 6.602
ORdate_year2002 8.820e+07 1.134e-08 0.0000 Inf
ORdate_year2003 1.506e+07 6.640e-08 0.0000 Inf
ORdate_year2004 1.364e+07 7.333e-08 0.0000 Inf
ORdate_year2005 2.984e+07 3.351e-08 0.0000 Inf
ORdate_year2006 1.029e+07 9.722e-08 0.0000 Inf
ORdate_year2007 1.913e+07 5.226e-08 0.0000 Inf
ORdate_year2008 2.135e+07 4.685e-08 0.0000 Inf
ORdate_year2009 2.216e+07 4.512e-08 0.0000 Inf
ORdate_year2010 2.415e+07 4.141e-08 0.0000 Inf
ORdate_year2011 3.463e-01 2.887e+00 0.0000 Inf
ORdate_year2012 3.973e-01 2.517e+00 0.0000 Inf
ORdate_year2013 3.464e-01 2.887e+00 0.0000 Inf
ORdate_year2014 4.442e-01 2.251e+00 0.0000 Inf
ORdate_year2015 1.215e+00 8.232e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.767 (se = 0.036 )
Likelihood ratio test= 27.76 on 17 df, p=0.05
Wald test = 12.32 on 17 df, p=0.8
Score (logrank) test = 26.43 on 17 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: COL4A1
Effect size...............: 0.135281
Standard error............: 0.402901
Odds ratio (effect size)..: 1.145
Lower 95% CI..............: 0.52
Upper 95% CI..............: 2.522
T-value...................: 0.335768
P-value...................: 0.7370459
Sample size in model......: 619
Number of events..........: 28
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 3.331e-01 1.395e+00 3.985e-01 0.836 0.40311
Age 7.850e-02 1.082e+00 2.569e-02 3.056 0.00224 **
Gendermale 8.319e-01 2.298e+00 5.448e-01 1.527 0.12674
ORdate_year2002 1.837e+01 9.490e+07 3.316e+04 0.001 0.99956
ORdate_year2003 1.663e+01 1.674e+07 3.316e+04 0.001 0.99960
ORdate_year2004 1.649e+01 1.447e+07 3.316e+04 0.000 0.99960
ORdate_year2005 1.727e+01 3.152e+07 3.316e+04 0.001 0.99958
ORdate_year2006 1.621e+01 1.099e+07 3.316e+04 0.000 0.99961
ORdate_year2007 1.692e+01 2.220e+07 3.316e+04 0.001 0.99959
ORdate_year2008 1.698e+01 2.376e+07 3.316e+04 0.001 0.99959
ORdate_year2009 1.697e+01 2.334e+07 3.316e+04 0.001 0.99959
ORdate_year2010 1.705e+01 2.541e+07 3.316e+04 0.001 0.99959
ORdate_year2011 -9.649e-01 3.810e-01 3.358e+04 0.000 0.99998
ORdate_year2012 -8.219e-01 4.396e-01 3.378e+04 0.000 0.99998
ORdate_year2013 -9.071e-01 4.037e-01 3.589e+04 0.000 0.99998
ORdate_year2014 -8.280e-01 4.369e-01 3.802e+04 0.000 0.99998
ORdate_year2015 2.565e-01 1.292e+00 3.666e+04 0.000 0.99999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.395e+00 7.167e-01 0.6390 3.047
Age 1.082e+00 9.245e-01 1.0286 1.138
Gendermale 2.298e+00 4.352e-01 0.7899 6.684
ORdate_year2002 9.490e+07 1.054e-08 0.0000 Inf
ORdate_year2003 1.674e+07 5.974e-08 0.0000 Inf
ORdate_year2004 1.447e+07 6.912e-08 0.0000 Inf
ORdate_year2005 3.152e+07 3.172e-08 0.0000 Inf
ORdate_year2006 1.099e+07 9.097e-08 0.0000 Inf
ORdate_year2007 2.220e+07 4.505e-08 0.0000 Inf
ORdate_year2008 2.376e+07 4.209e-08 0.0000 Inf
ORdate_year2009 2.334e+07 4.285e-08 0.0000 Inf
ORdate_year2010 2.541e+07 3.935e-08 0.0000 Inf
ORdate_year2011 3.810e-01 2.625e+00 0.0000 Inf
ORdate_year2012 4.396e-01 2.275e+00 0.0000 Inf
ORdate_year2013 4.037e-01 2.477e+00 0.0000 Inf
ORdate_year2014 4.369e-01 2.289e+00 0.0000 Inf
ORdate_year2015 1.292e+00 7.737e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.774 (se = 0.035 )
Likelihood ratio test= 28.36 on 17 df, p=0.04
Wald test = 12.83 on 17 df, p=0.7
Score (logrank) test = 27.02 on 17 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: COL4A2
Effect size...............: 0.333136
Standard error............: 0.398452
Odds ratio (effect size)..: 1.395
Lower 95% CI..............: 0.639
Upper 95% CI..............: 3.047
T-value...................: 0.836075
P-value...................: 0.4031125
Sample size in model......: 619
Number of events..........: 28
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -2.700e-01 7.634e-01 4.056e-01 -0.666 0.506
Age 8.056e-02 1.084e+00 2.608e-02 3.090 0.002 **
Gendermale 7.723e-01 2.165e+00 5.419e-01 1.425 0.154
ORdate_year2002 1.830e+01 8.849e+07 3.377e+04 0.001 1.000
ORdate_year2003 1.663e+01 1.669e+07 3.377e+04 0.000 1.000
ORdate_year2004 1.653e+01 1.511e+07 3.377e+04 0.000 1.000
ORdate_year2005 1.734e+01 3.381e+07 3.377e+04 0.001 1.000
ORdate_year2006 1.631e+01 1.207e+07 3.377e+04 0.000 1.000
ORdate_year2007 1.690e+01 2.190e+07 3.377e+04 0.001 1.000
ORdate_year2008 1.699e+01 2.398e+07 3.377e+04 0.001 1.000
ORdate_year2009 1.714e+01 2.770e+07 3.377e+04 0.001 1.000
ORdate_year2010 1.715e+01 2.816e+07 3.377e+04 0.001 1.000
ORdate_year2011 -9.726e-01 3.781e-01 3.420e+04 0.000 1.000
ORdate_year2012 -9.071e-01 4.037e-01 3.438e+04 0.000 1.000
ORdate_year2013 -8.924e-01 4.097e-01 3.661e+04 0.000 1.000
ORdate_year2014 -5.824e-01 5.585e-01 3.855e+04 0.000 1.000
ORdate_year2015 3.846e-01 1.469e+00 3.787e+04 0.000 1.000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 7.634e-01 1.310e+00 0.3447 1.690
Age 1.084e+00 9.226e-01 1.0299 1.141
Gendermale 2.165e+00 4.619e-01 0.7484 6.262
ORdate_year2002 8.849e+07 1.130e-08 0.0000 Inf
ORdate_year2003 1.669e+07 5.991e-08 0.0000 Inf
ORdate_year2004 1.511e+07 6.617e-08 0.0000 Inf
ORdate_year2005 3.381e+07 2.958e-08 0.0000 Inf
ORdate_year2006 1.207e+07 8.284e-08 0.0000 Inf
ORdate_year2007 2.190e+07 4.566e-08 0.0000 Inf
ORdate_year2008 2.398e+07 4.169e-08 0.0000 Inf
ORdate_year2009 2.770e+07 3.611e-08 0.0000 Inf
ORdate_year2010 2.816e+07 3.551e-08 0.0000 Inf
ORdate_year2011 3.781e-01 2.645e+00 0.0000 Inf
ORdate_year2012 4.037e-01 2.477e+00 0.0000 Inf
ORdate_year2013 4.097e-01 2.441e+00 0.0000 Inf
ORdate_year2014 5.585e-01 1.790e+00 0.0000 Inf
ORdate_year2015 1.469e+00 6.807e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.775 (se = 0.037 )
Likelihood ratio test= 28.1 on 17 df, p=0.04
Wald test = 12.78 on 17 df, p=0.8
Score (logrank) test = 27.17 on 17 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: LDLR
Effect size...............: -0.270027
Standard error............: 0.405629
Odds ratio (effect size)..: 0.763
Lower 95% CI..............: 0.345
Upper 95% CI..............: 1.69
T-value...................: -0.665698
P-value...................: 0.5056043
Sample size in model......: 619
Number of events..........: 28
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year, data = TEMP.DF)
n= 619, number of events= 28
(3 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.344e-01 1.144e+00 4.076e-01 0.330 0.74159
Age 7.970e-02 1.083e+00 2.565e-02 3.107 0.00189 **
Gendermale 8.000e-01 2.225e+00 5.425e-01 1.474 0.14036
ORdate_year2002 1.831e+01 8.979e+07 3.318e+04 0.001 0.99956
ORdate_year2003 1.654e+01 1.526e+07 3.318e+04 0.000 0.99960
ORdate_year2004 1.645e+01 1.392e+07 3.318e+04 0.000 0.99960
ORdate_year2005 1.722e+01 3.002e+07 3.318e+04 0.001 0.99959
ORdate_year2006 1.615e+01 1.031e+07 3.318e+04 0.000 0.99961
ORdate_year2007 1.678e+01 1.931e+07 3.318e+04 0.001 0.99960
ORdate_year2008 1.689e+01 2.166e+07 3.318e+04 0.001 0.99959
ORdate_year2009 1.692e+01 2.241e+07 3.318e+04 0.001 0.99959
ORdate_year2010 1.701e+01 2.429e+07 3.318e+04 0.001 0.99959
ORdate_year2011 -1.055e+00 3.483e-01 3.360e+04 0.000 0.99997
ORdate_year2012 -9.307e-01 3.943e-01 3.378e+04 0.000 0.99998
ORdate_year2013 -9.506e-01 3.865e-01 3.589e+04 0.000 0.99998
ORdate_year2014 -7.965e-01 4.509e-01 3.803e+04 0.000 0.99998
ORdate_year2015 2.056e-01 1.228e+00 3.690e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.144e+00 8.743e-01 0.5146 2.543
Age 1.083e+00 9.234e-01 1.0299 1.139
Gendermale 2.225e+00 4.493e-01 0.7684 6.445
ORdate_year2002 8.979e+07 1.114e-08 0.0000 Inf
ORdate_year2003 1.526e+07 6.554e-08 0.0000 Inf
ORdate_year2004 1.392e+07 7.186e-08 0.0000 Inf
ORdate_year2005 3.002e+07 3.331e-08 0.0000 Inf
ORdate_year2006 1.031e+07 9.703e-08 0.0000 Inf
ORdate_year2007 1.931e+07 5.178e-08 0.0000 Inf
ORdate_year2008 2.166e+07 4.618e-08 0.0000 Inf
ORdate_year2009 2.241e+07 4.463e-08 0.0000 Inf
ORdate_year2010 2.429e+07 4.118e-08 0.0000 Inf
ORdate_year2011 3.483e-01 2.871e+00 0.0000 Inf
ORdate_year2012 3.943e-01 2.536e+00 0.0000 Inf
ORdate_year2013 3.865e-01 2.587e+00 0.0000 Inf
ORdate_year2014 4.509e-01 2.218e+00 0.0000 Inf
ORdate_year2015 1.228e+00 8.142e-01 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Concordance= 0.766 (se = 0.037 )
Likelihood ratio test= 27.76 on 17 df, p=0.05
Wald test = 12.4 on 17 df, p=0.8
Score (logrank) test = 26.43 on 17 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: CD36
Effect size...............: 0.134388
Standard error............: 0.407554
Odds ratio (effect size)..: 1.144
Lower 95% CI..............: 0.515
Upper 95% CI..............: 2.543
T-value...................: 0.329743
P-value...................: 0.7415941
Sample size in model......: 619
Number of events..........: 28

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AERNASE.clin.targets.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.targets.COX.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Cox.2G.MODEL1.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
rowNames = FALSE, colNames = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.targets.COX.results)
Model 2
# Set up a dataframe to receive results
COX.results <- data.frame(matrix(NA, ncol = 12, nrow = 0))
# Looping over each target_of_interest/endpoint/time combination
for (i in 1:length(times)){
eptime = times[i]
ep = endpoints[i]
cat(paste0("* Analyzing the effect of plaque target-of-interest on [",ep,"].\n"))
cat(" - creating temporary SE for this work.\n")
TEMP.DF = as.data.frame(AERNASE.clin.targets)
cat(" - making a 'Surv' object and adding this to temporary dataframe.\n")
TEMP.DF$event <- as.integer(TEMP.DF[,ep])
#as.integer(TEMP.DF[,ep] == "Excluded")
TEMP.DF$y <- Surv(time = TEMP.DF[,eptime], event = TEMP.DF$event)
cat(" - making strata of each of the plaque target-of-interest and start survival analysis.\n")
for (target_of_interest in 1:length(TRAITS.TARGET.RANK)){
cat(paste0(" > processing [",TRAITS.TARGET.RANK[target_of_interest],"]; ",target_of_interest," out of ",length(TRAITS.TARGET.RANK)," target-of-interest.\n"))
# splitting into two groups
TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]] <- cut2(TEMP.DF[,TRAITS.TARGET.RANK[target_of_interest]], g = 2)
cat(paste0(" > cross tabulation of ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
show(table(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]))
cat(paste0("\n > fitting the model for ",TRAITS.TARGET.RANK[target_of_interest],"-stratum.\n"))
fit <- survfit(as.formula(paste0("y ~ ", TRAITS.TARGET.RANK[target_of_interest])), data = TEMP.DF)
cat(paste0("\n > make a Kaplan-Meier-shizzle...\n"))
# make Kaplan-Meier curve and save it
show(ggsurvplot(fit, data = TEMP.DF,
palette = c("#DB003F", "#1290D9"),
# palete = c("F59D10", "#DB003F", "#49A01D", "#1290D9"),
linetype = c(1,2),
# linetype = c(1,2,3,4),
# conf.int = FALSE, conf.int.fill = "#595A5C", conf.int.alpha = 0.1,
pval = FALSE, pval.method = FALSE, pval.size = 4,
risk.table = TRUE, risk.table.y.text = FALSE, tables.y.text.col = TRUE, fontsize = 4,
censor = FALSE,
legend = "right",
legend.title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
legend.labs = c("low", "high"),
title = paste0("Risk of ",ep,""), xlab = "Time [years]", font.main = c(16, "bold", "black")))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.targets.survival.",ep,".2G.",
TRAITS.TARGET.RANK[target_of_interest],".pdf"), width = 12, height = 10, onefile = FALSE)
cat(paste0("\n > perform the Cox-regression fashizzle and plot it...\n"))
### Do Cox-regression and plot it
### MODEL 2 adjusted for age, sex, hypertension, diabetes, smoking, LDL-C levels, lipid-lowering drugs, antiplatelet drugs, eGFR, BMI, history of CVD, level of stenosis
cox = coxph(Surv(TEMP.DF[,eptime], event) ~ TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]]+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
coxplot = coxph(Surv(TEMP.DF[,eptime], event) ~ strata(TEMP.DF[[ TRAITS.TARGET.RANK[target_of_interest] ]])+Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus + SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD + BMI + MedHx_CVD + stenose, data = TEMP.DF)
plot(survfit(coxplot), main = paste0("Cox proportional hazard of [",ep,"] per [",eptime,"]."),
# ylim = c(0.2, 1), xlim = c(0,3), col = c("#595A5C", "#DB003F", "#1290D9"),
ylim = c(0, 1), xlim = c(0,3), col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
ylab = "Suvival probability", xlab = "FU time [years]",
mark.time = FALSE, axes = FALSE, bty = "n")
legend("topright",
c("low", "high"),
title = paste0("",TRAITS.TARGET.RANK[target_of_interest],""),
col = c("#DB003F", "#1290D9"),
lty = c(1,2), lwd = 2,
bty = "n")
axis(side = 1, at = seq(0, 3, by = 1))
axis(side = 2, at = seq(0, 1, by = 0.2))
dev.copy2pdf(file = paste0(COX_loc,"/",
Today,".AERNASE.clin.targets.Cox.",ep,".2G.",
# Today,".AERNASE.clin.targets.Cox.",ep,".4G.",
TRAITS.TARGET.RANK[target_of_interest],".MODEL2.pdf"), height = 12, width = 10, onefile = TRUE)
show(summary(cox))
cat(paste0("\n > writing the Cox-regression fashizzle to Excel...\n"))
COX.results.TEMP <- data.frame(matrix(NA, ncol = 12, nrow = 0))
COX.results.TEMP[1,] = COX.STAT(cox, "AERNASE.clin.targets", ep, TRAITS.TARGET.RANK[target_of_interest])
COX.results = rbind(COX.results, COX.results.TEMP)
}
}
* Analyzing the effect of plaque target-of-interest on [epmajor.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 68
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 2.810e-01 1.324e+00 2.599e-01 1.081 0.27971
Age 4.957e-02 1.051e+00 1.823e-02 2.719 0.00655 **
Gendermale 7.662e-01 2.152e+00 3.682e-01 2.081 0.03742 *
ORdate_year2002 1.517e+01 3.874e+06 1.004e+04 0.002 0.99879
ORdate_year2003 1.405e+01 1.265e+06 1.004e+04 0.001 0.99888
ORdate_year2004 1.447e+01 1.926e+06 1.004e+04 0.001 0.99885
ORdate_year2005 1.493e+01 3.058e+06 1.004e+04 0.001 0.99881
ORdate_year2006 1.495e+01 3.117e+06 1.004e+04 0.001 0.99881
ORdate_year2007 1.414e+01 1.378e+06 1.004e+04 0.001 0.99888
ORdate_year2008 1.514e+01 3.765e+06 1.004e+04 0.002 0.99880
ORdate_year2009 1.401e+01 1.217e+06 1.004e+04 0.001 0.99889
ORdate_year2010 1.475e+01 2.533e+06 1.004e+04 0.001 0.99883
ORdate_year2011 1.400e+01 1.203e+06 1.004e+04 0.001 0.99889
ORdate_year2012 1.472e+01 2.465e+06 1.004e+04 0.001 0.99883
ORdate_year2013 -1.539e+00 2.146e-01 1.105e+04 0.000 0.99989
ORdate_year2014 -1.430e+00 2.394e-01 1.419e+04 0.000 0.99992
ORdate_year2015 -9.228e-01 3.974e-01 1.108e+04 0.000 0.99993
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -6.833e-01 5.049e-01 5.413e-01 -1.262 0.20681
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.046e-01 2.023e+00 2.684e-01 2.625 0.00867 **
SmokerStatusEx-smoker -5.258e-01 5.911e-01 2.718e-01 -1.934 0.05306 .
SmokerStatusNever smoked -8.880e-01 4.115e-01 4.387e-01 -2.024 0.04297 *
Med.Statin.LLDno 2.797e-01 1.323e+00 2.911e-01 0.961 0.33675
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.726e-01 1.188e+00 3.980e-01 0.434 0.66448
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -6.848e-03 9.932e-01 6.271e-03 -1.092 0.27485
BMI 5.338e-02 1.055e+00 3.118e-02 1.712 0.08693 .
MedHx_CVDNo -6.161e-01 5.400e-01 2.962e-01 -2.080 0.03751 *
stenose0-49% 3.449e-01 1.412e+00 1.155e+04 0.000 0.99998
stenose50-70% 1.589e+01 7.963e+06 1.004e+04 0.002 0.99874
stenose70-90% 1.649e+01 1.443e+07 1.004e+04 0.002 0.99869
stenose90-99% 1.653e+01 1.513e+07 1.004e+04 0.002 0.99869
stenose100% (Occlusion) 8.449e-01 2.328e+00 1.105e+04 0.000 0.99994
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.324e+00 7.550e-01 0.7958 2.2043
Age 1.051e+00 9.516e-01 1.0139 1.0890
Gendermale 2.152e+00 4.648e-01 1.0456 4.4276
ORdate_year2002 3.874e+06 2.581e-07 0.0000 Inf
ORdate_year2003 1.265e+06 7.908e-07 0.0000 Inf
ORdate_year2004 1.926e+06 5.192e-07 0.0000 Inf
ORdate_year2005 3.058e+06 3.270e-07 0.0000 Inf
ORdate_year2006 3.117e+06 3.208e-07 0.0000 Inf
ORdate_year2007 1.378e+06 7.257e-07 0.0000 Inf
ORdate_year2008 3.765e+06 2.656e-07 0.0000 Inf
ORdate_year2009 1.217e+06 8.216e-07 0.0000 Inf
ORdate_year2010 2.533e+06 3.947e-07 0.0000 Inf
ORdate_year2011 1.203e+06 8.311e-07 0.0000 Inf
ORdate_year2012 2.465e+06 4.056e-07 0.0000 Inf
ORdate_year2013 2.146e-01 4.661e+00 0.0000 Inf
ORdate_year2014 2.394e-01 4.177e+00 0.0000 Inf
ORdate_year2015 3.974e-01 2.516e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 5.049e-01 1.981e+00 0.1748 1.4588
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.023e+00 4.943e-01 1.1954 3.4237
SmokerStatusEx-smoker 5.911e-01 1.692e+00 0.3470 1.0070
SmokerStatusNever smoked 4.115e-01 2.430e+00 0.1742 0.9723
Med.Statin.LLDno 1.323e+00 7.560e-01 0.7476 2.3402
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.188e+00 8.415e-01 0.5448 2.5925
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.932e-01 1.007e+00 0.9810 1.0055
BMI 1.055e+00 9.480e-01 0.9923 1.1213
MedHx_CVDNo 5.400e-01 1.852e+00 0.3022 0.9650
stenose0-49% 1.412e+00 7.083e-01 0.0000 Inf
stenose50-70% 7.963e+06 1.256e-07 0.0000 Inf
stenose70-90% 1.443e+07 6.928e-08 0.0000 Inf
stenose90-99% 1.513e+07 6.611e-08 0.0000 Inf
stenose100% (Occlusion) 2.328e+00 4.296e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.763 (se = 0.026 )
Likelihood ratio test= 58.67 on 31 df, p=0.002
Wald test = 42.21 on 31 df, p=0.09
Score (logrank) test = 57.13 on 31 df, p=0.003
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: CXCL10
Effect size...............: 0.280972
Standard error............: 0.259924
Odds ratio (effect size)..: 1.324
Lower 95% CI..............: 0.796
Upper 95% CI..............: 2.204
T-value...................: 1.080978
P-value...................: 0.2797069
Sample size in model......: 540
Number of events..........: 68
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 68
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] -1.939e-01 8.237e-01 2.933e-01 -0.661 0.5085
Age 4.808e-02 1.049e+00 1.810e-02 2.656 0.0079 **
Gendermale 7.586e-01 2.135e+00 3.684e-01 2.059 0.0395 *
ORdate_year2002 1.524e+01 4.172e+06 9.999e+03 0.002 0.9988
ORdate_year2003 1.420e+01 1.463e+06 9.999e+03 0.001 0.9989
ORdate_year2004 1.464e+01 2.289e+06 9.999e+03 0.001 0.9988
ORdate_year2005 1.508e+01 3.539e+06 9.999e+03 0.002 0.9988
ORdate_year2006 1.509e+01 3.560e+06 9.999e+03 0.002 0.9988
ORdate_year2007 1.431e+01 1.633e+06 9.999e+03 0.001 0.9989
ORdate_year2008 1.525e+01 4.192e+06 9.999e+03 0.002 0.9988
ORdate_year2009 1.420e+01 1.462e+06 9.999e+03 0.001 0.9989
ORdate_year2010 1.494e+01 3.092e+06 9.999e+03 0.001 0.9988
ORdate_year2011 1.416e+01 1.411e+06 9.999e+03 0.001 0.9989
ORdate_year2012 1.489e+01 2.933e+06 9.999e+03 0.001 0.9988
ORdate_year2013 -1.413e+00 2.433e-01 1.097e+04 0.000 0.9999
ORdate_year2014 -1.166e+00 3.117e-01 1.414e+04 0.000 0.9999
ORdate_year2015 -8.525e-01 4.263e-01 1.108e+04 0.000 0.9999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -6.978e-01 4.977e-01 5.404e-01 -1.291 0.1966
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.125e-01 2.039e+00 2.687e-01 2.652 0.0080 **
SmokerStatusEx-smoker -5.294e-01 5.889e-01 2.716e-01 -1.949 0.0512 .
SmokerStatusNever smoked -8.991e-01 4.069e-01 4.382e-01 -2.052 0.0402 *
Med.Statin.LLDno 2.900e-01 1.336e+00 2.895e-01 1.002 0.3164
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.598e-01 1.173e+00 3.986e-01 0.401 0.6885
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -6.447e-03 9.936e-01 6.238e-03 -1.033 0.3014
BMI 5.409e-02 1.056e+00 3.146e-02 1.720 0.0855 .
MedHx_CVDNo -6.195e-01 5.382e-01 2.953e-01 -2.098 0.0359 *
stenose0-49% 6.280e-01 1.874e+00 1.144e+04 0.000 1.0000
stenose50-70% 1.602e+01 9.074e+06 9.999e+03 0.002 0.9987
stenose70-90% 1.667e+01 1.730e+07 9.999e+03 0.002 0.9987
stenose90-99% 1.671e+01 1.812e+07 9.999e+03 0.002 0.9987
stenose100% (Occlusion) 1.079e+00 2.942e+00 1.091e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 8.237e-01 1.214e+00 0.4636 1.4636
Age 1.049e+00 9.531e-01 1.0127 1.0871
Gendermale 2.135e+00 4.683e-01 1.0372 4.3963
ORdate_year2002 4.172e+06 2.397e-07 0.0000 Inf
ORdate_year2003 1.463e+06 6.836e-07 0.0000 Inf
ORdate_year2004 2.289e+06 4.369e-07 0.0000 Inf
ORdate_year2005 3.539e+06 2.825e-07 0.0000 Inf
ORdate_year2006 3.560e+06 2.809e-07 0.0000 Inf
ORdate_year2007 1.633e+06 6.123e-07 0.0000 Inf
ORdate_year2008 4.192e+06 2.385e-07 0.0000 Inf
ORdate_year2009 1.462e+06 6.841e-07 0.0000 Inf
ORdate_year2010 3.092e+06 3.234e-07 0.0000 Inf
ORdate_year2011 1.411e+06 7.088e-07 0.0000 Inf
ORdate_year2012 2.933e+06 3.410e-07 0.0000 Inf
ORdate_year2013 2.433e-01 4.110e+00 0.0000 Inf
ORdate_year2014 3.117e-01 3.208e+00 0.0000 Inf
ORdate_year2015 4.263e-01 2.346e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.977e-01 2.009e+00 0.1726 1.4352
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.039e+00 4.904e-01 1.2044 3.4525
SmokerStatusEx-smoker 5.889e-01 1.698e+00 0.3458 1.0029
SmokerStatusNever smoked 4.069e-01 2.457e+00 0.1724 0.9605
Med.Statin.LLDno 1.336e+00 7.482e-01 0.7578 2.3572
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.173e+00 8.523e-01 0.5372 2.5627
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.936e-01 1.006e+00 0.9815 1.0058
BMI 1.056e+00 9.473e-01 0.9925 1.1227
MedHx_CVDNo 5.382e-01 1.858e+00 0.3017 0.9601
stenose0-49% 1.874e+00 5.336e-01 0.0000 Inf
stenose50-70% 9.074e+06 1.102e-07 0.0000 Inf
stenose70-90% 1.730e+07 5.779e-08 0.0000 Inf
stenose90-99% 1.812e+07 5.520e-08 0.0000 Inf
stenose100% (Occlusion) 2.942e+00 3.399e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.759 (se = 0.026 )
Likelihood ratio test= 57.98 on 31 df, p=0.002
Wald test = 41.62 on 31 df, p=0.1
Score (logrank) test = 56.2 on 31 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: PCSK9
Effect size...............: -0.193917
Standard error............: 0.293271
Odds ratio (effect size)..: 0.824
Lower 95% CI..............: 0.464
Upper 95% CI..............: 1.464
T-value...................: -0.661222
P-value...................: 0.5084699
Sample size in model......: 540
Number of events..........: 68
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 68
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 3.032e-01 1.354e+00 2.553e-01 1.187 0.23513
Age 4.797e-02 1.049e+00 1.798e-02 2.667 0.00765 **
Gendermale 7.804e-01 2.182e+00 3.702e-01 2.108 0.03502 *
ORdate_year2002 1.544e+01 5.089e+06 1.003e+04 0.002 0.99877
ORdate_year2003 1.426e+01 1.557e+06 1.003e+04 0.001 0.99887
ORdate_year2004 1.468e+01 2.365e+06 1.003e+04 0.001 0.99883
ORdate_year2005 1.516e+01 3.831e+06 1.003e+04 0.002 0.99879
ORdate_year2006 1.516e+01 3.831e+06 1.003e+04 0.002 0.99879
ORdate_year2007 1.437e+01 1.740e+06 1.003e+04 0.001 0.99886
ORdate_year2008 1.535e+01 4.643e+06 1.003e+04 0.002 0.99878
ORdate_year2009 1.419e+01 1.452e+06 1.003e+04 0.001 0.99887
ORdate_year2010 1.496e+01 3.147e+06 1.003e+04 0.001 0.99881
ORdate_year2011 1.424e+01 1.534e+06 1.003e+04 0.001 0.99887
ORdate_year2012 1.499e+01 3.251e+06 1.003e+04 0.001 0.99881
ORdate_year2013 -1.376e+00 2.526e-01 1.102e+04 0.000 0.99990
ORdate_year2014 -1.182e+00 3.068e-01 1.418e+04 0.000 0.99993
ORdate_year2015 -8.361e-01 4.334e-01 1.099e+04 0.000 0.99994
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -6.779e-01 5.077e-01 5.408e-01 -1.254 0.20997
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 6.949e-01 2.004e+00 2.681e-01 2.592 0.00954 **
SmokerStatusEx-smoker -5.261e-01 5.909e-01 2.711e-01 -1.941 0.05230 .
SmokerStatusNever smoked -8.982e-01 4.073e-01 4.407e-01 -2.038 0.04152 *
Med.Statin.LLDno 2.979e-01 1.347e+00 2.905e-01 1.026 0.30504
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.087e-01 1.232e+00 3.973e-01 0.525 0.59939
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -7.170e-03 9.929e-01 6.253e-03 -1.147 0.25151
BMI 5.642e-02 1.058e+00 3.117e-02 1.810 0.07028 .
MedHx_CVDNo -6.374e-01 5.287e-01 2.961e-01 -2.153 0.03134 *
stenose0-49% 3.902e-01 1.477e+00 1.150e+04 0.000 0.99997
stenose50-70% 1.584e+01 7.580e+06 1.003e+04 0.002 0.99874
stenose70-90% 1.647e+01 1.429e+07 1.003e+04 0.002 0.99869
stenose90-99% 1.652e+01 1.497e+07 1.003e+04 0.002 0.99869
stenose100% (Occlusion) 8.703e-01 2.388e+00 1.104e+04 0.000 0.99994
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.354e+00 7.385e-01 0.8209 2.2336
Age 1.049e+00 9.532e-01 1.0128 1.0868
Gendermale 2.182e+00 4.582e-01 1.0564 4.5080
ORdate_year2002 5.089e+06 1.965e-07 0.0000 Inf
ORdate_year2003 1.557e+06 6.423e-07 0.0000 Inf
ORdate_year2004 2.365e+06 4.228e-07 0.0000 Inf
ORdate_year2005 3.831e+06 2.610e-07 0.0000 Inf
ORdate_year2006 3.831e+06 2.610e-07 0.0000 Inf
ORdate_year2007 1.740e+06 5.747e-07 0.0000 Inf
ORdate_year2008 4.643e+06 2.154e-07 0.0000 Inf
ORdate_year2009 1.452e+06 6.889e-07 0.0000 Inf
ORdate_year2010 3.147e+06 3.177e-07 0.0000 Inf
ORdate_year2011 1.534e+06 6.520e-07 0.0000 Inf
ORdate_year2012 3.251e+06 3.076e-07 0.0000 Inf
ORdate_year2013 2.526e-01 3.959e+00 0.0000 Inf
ORdate_year2014 3.068e-01 3.259e+00 0.0000 Inf
ORdate_year2015 4.334e-01 2.307e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 5.077e-01 1.970e+00 0.1759 1.4651
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.004e+00 4.991e-01 1.1846 3.3887
SmokerStatusEx-smoker 5.909e-01 1.692e+00 0.3474 1.0052
SmokerStatusNever smoked 4.073e-01 2.455e+00 0.1717 0.9661
Med.Statin.LLDno 1.347e+00 7.424e-01 0.7623 2.3802
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.232e+00 8.116e-01 0.5655 2.6843
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.929e-01 1.007e+00 0.9808 1.0051
BMI 1.058e+00 9.451e-01 0.9953 1.1247
MedHx_CVDNo 5.287e-01 1.892e+00 0.2959 0.9445
stenose0-49% 1.477e+00 6.769e-01 0.0000 Inf
stenose50-70% 7.580e+06 1.319e-07 0.0000 Inf
stenose70-90% 1.429e+07 7.000e-08 0.0000 Inf
stenose90-99% 1.497e+07 6.681e-08 0.0000 Inf
stenose100% (Occlusion) 2.388e+00 4.188e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.765 (se = 0.026 )
Likelihood ratio test= 58.94 on 31 df, p=0.002
Wald test = 43.05 on 31 df, p=0.07
Score (logrank) test = 57.06 on 31 df, p=0.003
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: COL4A1
Effect size...............: 0.303159
Standard error............: 0.255344
Odds ratio (effect size)..: 1.354
Lower 95% CI..............: 0.821
Upper 95% CI..............: 2.234
T-value...................: 1.187256
P-value...................: 0.2351266
Sample size in model......: 540
Number of events..........: 68
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 68
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 5.008e-01 1.650e+00 2.577e-01 1.943 0.05197 .
Age 4.815e-02 1.049e+00 1.807e-02 2.665 0.00771 **
Gendermale 8.151e-01 2.259e+00 3.715e-01 2.194 0.02825 *
ORdate_year2002 1.554e+01 5.607e+06 1.012e+04 0.002 0.99877
ORdate_year2003 1.433e+01 1.667e+06 1.012e+04 0.001 0.99887
ORdate_year2004 1.474e+01 2.529e+06 1.012e+04 0.001 0.99884
ORdate_year2005 1.519e+01 3.941e+06 1.012e+04 0.002 0.99880
ORdate_year2006 1.521e+01 4.024e+06 1.012e+04 0.002 0.99880
ORdate_year2007 1.453e+01 2.039e+06 1.012e+04 0.001 0.99885
ORdate_year2008 1.546e+01 5.198e+06 1.012e+04 0.002 0.99878
ORdate_year2009 1.421e+01 1.486e+06 1.012e+04 0.001 0.99888
ORdate_year2010 1.502e+01 3.342e+06 1.012e+04 0.001 0.99882
ORdate_year2011 1.430e+01 1.624e+06 1.012e+04 0.001 0.99887
ORdate_year2012 1.507e+01 3.504e+06 1.012e+04 0.001 0.99881
ORdate_year2013 -1.234e+00 2.910e-01 1.121e+04 0.000 0.99991
ORdate_year2014 -1.205e+00 2.997e-01 1.430e+04 0.000 0.99993
ORdate_year2015 -7.986e-01 4.500e-01 1.102e+04 0.000 0.99994
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -6.539e-01 5.200e-01 5.394e-01 -1.212 0.22535
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.286e-01 2.072e+00 2.705e-01 2.693 0.00708 **
SmokerStatusEx-smoker -5.184e-01 5.954e-01 2.716e-01 -1.909 0.05626 .
SmokerStatusNever smoked -8.878e-01 4.116e-01 4.397e-01 -2.019 0.04348 *
Med.Statin.LLDno 3.122e-01 1.366e+00 2.902e-01 1.076 0.28195
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.813e-01 1.199e+00 3.977e-01 0.456 0.64848
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -7.681e-03 9.923e-01 6.211e-03 -1.237 0.21620
BMI 5.853e-02 1.060e+00 3.110e-02 1.882 0.05986 .
MedHx_CVDNo -6.134e-01 5.415e-01 2.967e-01 -2.067 0.03873 *
stenose0-49% 3.614e-01 1.435e+00 1.171e+04 0.000 0.99998
stenose50-70% 1.575e+01 6.905e+06 1.012e+04 0.002 0.99876
stenose70-90% 1.643e+01 1.364e+07 1.012e+04 0.002 0.99870
stenose90-99% 1.643e+01 1.368e+07 1.012e+04 0.002 0.99870
stenose100% (Occlusion) 8.084e-01 2.244e+00 1.119e+04 0.000 0.99994
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.650e+00 6.061e-01 0.9957 2.7340
Age 1.049e+00 9.530e-01 1.0128 1.0872
Gendermale 2.259e+00 4.426e-01 1.0908 4.6802
ORdate_year2002 5.607e+06 1.783e-07 0.0000 Inf
ORdate_year2003 1.667e+06 5.999e-07 0.0000 Inf
ORdate_year2004 2.529e+06 3.954e-07 0.0000 Inf
ORdate_year2005 3.941e+06 2.537e-07 0.0000 Inf
ORdate_year2006 4.024e+06 2.485e-07 0.0000 Inf
ORdate_year2007 2.039e+06 4.904e-07 0.0000 Inf
ORdate_year2008 5.198e+06 1.924e-07 0.0000 Inf
ORdate_year2009 1.486e+06 6.729e-07 0.0000 Inf
ORdate_year2010 3.342e+06 2.992e-07 0.0000 Inf
ORdate_year2011 1.624e+06 6.159e-07 0.0000 Inf
ORdate_year2012 3.504e+06 2.854e-07 0.0000 Inf
ORdate_year2013 2.910e-01 3.436e+00 0.0000 Inf
ORdate_year2014 2.997e-01 3.337e+00 0.0000 Inf
ORdate_year2015 4.500e-01 2.222e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 5.200e-01 1.923e+00 0.1807 1.4966
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.072e+00 4.826e-01 1.2194 3.5213
SmokerStatusEx-smoker 5.954e-01 1.679e+00 0.3497 1.0139
SmokerStatusNever smoked 4.116e-01 2.430e+00 0.1738 0.9743
Med.Statin.LLDno 1.366e+00 7.318e-01 0.7737 2.4132
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.199e+00 8.342e-01 0.5498 2.6138
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.923e-01 1.008e+00 0.9803 1.0045
BMI 1.060e+00 9.432e-01 0.9976 1.1269
MedHx_CVDNo 5.415e-01 1.847e+00 0.3027 0.9687
stenose0-49% 1.435e+00 6.967e-01 0.0000 Inf
stenose50-70% 6.905e+06 1.448e-07 0.0000 Inf
stenose70-90% 1.364e+07 7.333e-08 0.0000 Inf
stenose90-99% 1.368e+07 7.309e-08 0.0000 Inf
stenose100% (Occlusion) 2.244e+00 4.455e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.772 (se = 0.025 )
Likelihood ratio test= 61.36 on 31 df, p=9e-04
Wald test = 45.77 on 31 df, p=0.04
Score (logrank) test = 59.38 on 31 df, p=0.002
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: COL4A2
Effect size...............: 0.500752
Standard error............: 0.257669
Odds ratio (effect size)..: 1.65
Lower 95% CI..............: 0.996
Upper 95% CI..............: 2.734
T-value...................: 1.943392
P-value...................: 0.05196879
Sample size in model......: 540
Number of events..........: 68
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 68
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -6.001e-02 9.418e-01 2.536e-01 -0.237 0.81297
Age 4.831e-02 1.049e+00 1.817e-02 2.659 0.00783 **
Gendermale 7.729e-01 2.166e+00 3.679e-01 2.101 0.03564 *
ORdate_year2002 1.525e+01 4.193e+06 9.994e+03 0.002 0.99878
ORdate_year2003 1.415e+01 1.404e+06 9.994e+03 0.001 0.99887
ORdate_year2004 1.461e+01 2.220e+06 9.994e+03 0.001 0.99883
ORdate_year2005 1.508e+01 3.546e+06 9.994e+03 0.002 0.99880
ORdate_year2006 1.506e+01 3.485e+06 9.994e+03 0.002 0.99880
ORdate_year2007 1.428e+01 1.586e+06 9.994e+03 0.001 0.99886
ORdate_year2008 1.522e+01 4.082e+06 9.994e+03 0.002 0.99878
ORdate_year2009 1.417e+01 1.424e+06 9.994e+03 0.001 0.99887
ORdate_year2010 1.491e+01 3.000e+06 9.994e+03 0.001 0.99881
ORdate_year2011 1.411e+01 1.343e+06 9.994e+03 0.001 0.99887
ORdate_year2012 1.485e+01 2.816e+06 9.994e+03 0.001 0.99881
ORdate_year2013 -1.427e+00 2.401e-01 1.097e+04 0.000 0.99990
ORdate_year2014 -1.102e+00 3.321e-01 1.413e+04 0.000 0.99994
ORdate_year2015 -8.796e-01 4.150e-01 1.106e+04 0.000 0.99994
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -7.099e-01 4.917e-01 5.409e-01 -1.312 0.18937
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.073e-01 2.029e+00 2.684e-01 2.635 0.00841 **
SmokerStatusEx-smoker -5.395e-01 5.830e-01 2.710e-01 -1.991 0.04653 *
SmokerStatusNever smoked -8.870e-01 4.119e-01 4.387e-01 -2.022 0.04321 *
Med.Statin.LLDno 2.818e-01 1.325e+00 2.905e-01 0.970 0.33215
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.698e-01 1.185e+00 4.003e-01 0.424 0.67137
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -6.465e-03 9.936e-01 6.224e-03 -1.039 0.29897
BMI 5.354e-02 1.055e+00 3.137e-02 1.707 0.08788 .
MedHx_CVDNo -6.167e-01 5.397e-01 2.963e-01 -2.082 0.03737 *
stenose0-49% 5.378e-01 1.712e+00 1.133e+04 0.000 0.99996
stenose50-70% 1.599e+01 8.834e+06 9.994e+03 0.002 0.99872
stenose70-90% 1.662e+01 1.654e+07 9.994e+03 0.002 0.99867
stenose90-99% 1.666e+01 1.713e+07 9.994e+03 0.002 0.99867
stenose100% (Occlusion) 1.039e+00 2.827e+00 1.090e+04 0.000 0.99992
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 9.418e-01 1.062e+00 0.5729 1.5482
Age 1.049e+00 9.528e-01 1.0128 1.0875
Gendermale 2.166e+00 4.617e-01 1.0532 4.4543
ORdate_year2002 4.193e+06 2.385e-07 0.0000 Inf
ORdate_year2003 1.404e+06 7.125e-07 0.0000 Inf
ORdate_year2004 2.220e+06 4.504e-07 0.0000 Inf
ORdate_year2005 3.546e+06 2.820e-07 0.0000 Inf
ORdate_year2006 3.485e+06 2.869e-07 0.0000 Inf
ORdate_year2007 1.586e+06 6.303e-07 0.0000 Inf
ORdate_year2008 4.082e+06 2.450e-07 0.0000 Inf
ORdate_year2009 1.424e+06 7.022e-07 0.0000 Inf
ORdate_year2010 3.000e+06 3.334e-07 0.0000 Inf
ORdate_year2011 1.343e+06 7.447e-07 0.0000 Inf
ORdate_year2012 2.816e+06 3.551e-07 0.0000 Inf
ORdate_year2013 2.401e-01 4.164e+00 0.0000 Inf
ORdate_year2014 3.321e-01 3.011e+00 0.0000 Inf
ORdate_year2015 4.150e-01 2.410e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.917e-01 2.034e+00 0.1703 1.4194
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.029e+00 4.930e-01 1.1987 3.4328
SmokerStatusEx-smoker 5.830e-01 1.715e+00 0.3427 0.9918
SmokerStatusNever smoked 4.119e-01 2.428e+00 0.1743 0.9733
Med.Statin.LLDno 1.325e+00 7.545e-01 0.7500 2.3424
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.185e+00 8.438e-01 0.5408 2.5970
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.936e-01 1.006e+00 0.9815 1.0058
BMI 1.055e+00 9.479e-01 0.9921 1.1219
MedHx_CVDNo 5.397e-01 1.853e+00 0.3020 0.9646
stenose0-49% 1.712e+00 5.840e-01 0.0000 Inf
stenose50-70% 8.834e+06 1.132e-07 0.0000 Inf
stenose70-90% 1.654e+07 6.047e-08 0.0000 Inf
stenose90-99% 1.713e+07 5.838e-08 0.0000 Inf
stenose100% (Occlusion) 2.827e+00 3.537e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.761 (se = 0.025 )
Likelihood ratio test= 57.58 on 31 df, p=0.003
Wald test = 41.44 on 31 df, p=0.1
Score (logrank) test = 55.85 on 31 df, p=0.004
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: LDLR
Effect size...............: -0.060007
Standard error............: 0.253618
Odds ratio (effect size)..: 0.942
Lower 95% CI..............: 0.573
Upper 95% CI..............: 1.548
T-value...................: -0.236602
P-value...................: 0.8129655
Sample size in model......: 540
Number of events..........: 68
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 68
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 3.332e-01 1.395e+00 2.556e-01 1.303 0.19241
Age 4.768e-02 1.049e+00 1.793e-02 2.659 0.00785 **
Gendermale 7.949e-01 2.214e+00 3.698e-01 2.149 0.03161 *
ORdate_year2002 1.545e+01 5.148e+06 1.002e+04 0.002 0.99877
ORdate_year2003 1.429e+01 1.604e+06 1.002e+04 0.001 0.99886
ORdate_year2004 1.472e+01 2.475e+06 1.002e+04 0.001 0.99883
ORdate_year2005 1.517e+01 3.891e+06 1.002e+04 0.002 0.99879
ORdate_year2006 1.516e+01 3.828e+06 1.002e+04 0.002 0.99879
ORdate_year2007 1.439e+01 1.771e+06 1.002e+04 0.001 0.99885
ORdate_year2008 1.537e+01 4.712e+06 1.002e+04 0.002 0.99878
ORdate_year2009 1.420e+01 1.466e+06 1.002e+04 0.001 0.99887
ORdate_year2010 1.499e+01 3.251e+06 1.002e+04 0.001 0.99881
ORdate_year2011 1.425e+01 1.551e+06 1.002e+04 0.001 0.99886
ORdate_year2012 1.493e+01 3.038e+06 1.002e+04 0.001 0.99881
ORdate_year2013 -1.144e+00 3.187e-01 1.100e+04 0.000 0.99992
ORdate_year2014 -1.191e+00 3.039e-01 1.417e+04 0.000 0.99993
ORdate_year2015 -8.567e-01 4.245e-01 1.097e+04 0.000 0.99994
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -6.757e-01 5.088e-01 5.405e-01 -1.250 0.21125
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 7.292e-01 2.073e+00 2.686e-01 2.714 0.00664 **
SmokerStatusEx-smoker -5.577e-01 5.725e-01 2.711e-01 -2.057 0.03966 *
SmokerStatusNever smoked -8.788e-01 4.153e-01 4.404e-01 -1.995 0.04600 *
Med.Statin.LLDno 2.874e-01 1.333e+00 2.898e-01 0.992 0.32136
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.318e-01 1.261e+00 3.983e-01 0.582 0.56051
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -7.560e-03 9.925e-01 6.342e-03 -1.192 0.23324
BMI 5.402e-02 1.056e+00 3.132e-02 1.725 0.08454 .
MedHx_CVDNo -6.307e-01 5.322e-01 2.961e-01 -2.130 0.03314 *
stenose0-49% 3.077e-01 1.360e+00 1.152e+04 0.000 0.99998
stenose50-70% 1.579e+01 7.222e+06 1.002e+04 0.002 0.99874
stenose70-90% 1.640e+01 1.330e+07 1.002e+04 0.002 0.99869
stenose90-99% 1.645e+01 1.396e+07 1.002e+04 0.002 0.99869
stenose100% (Occlusion) 7.952e-01 2.215e+00 1.104e+04 0.000 0.99994
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.395e+00 7.166e-01 0.8455 2.3032
Age 1.049e+00 9.534e-01 1.0126 1.0863
Gendermale 2.214e+00 4.516e-01 1.0726 4.5713
ORdate_year2002 5.148e+06 1.943e-07 0.0000 Inf
ORdate_year2003 1.604e+06 6.233e-07 0.0000 Inf
ORdate_year2004 2.475e+06 4.040e-07 0.0000 Inf
ORdate_year2005 3.891e+06 2.570e-07 0.0000 Inf
ORdate_year2006 3.828e+06 2.612e-07 0.0000 Inf
ORdate_year2007 1.771e+06 5.647e-07 0.0000 Inf
ORdate_year2008 4.712e+06 2.122e-07 0.0000 Inf
ORdate_year2009 1.466e+06 6.820e-07 0.0000 Inf
ORdate_year2010 3.251e+06 3.076e-07 0.0000 Inf
ORdate_year2011 1.551e+06 6.447e-07 0.0000 Inf
ORdate_year2012 3.038e+06 3.292e-07 0.0000 Inf
ORdate_year2013 3.187e-01 3.138e+00 0.0000 Inf
ORdate_year2014 3.039e-01 3.290e+00 0.0000 Inf
ORdate_year2015 4.245e-01 2.355e+00 0.0000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 5.088e-01 1.965e+00 0.1764 1.4677
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.073e+00 4.823e-01 1.2246 3.5102
SmokerStatusEx-smoker 5.725e-01 1.747e+00 0.3365 0.9740
SmokerStatusNever smoked 4.153e-01 2.408e+00 0.1752 0.9845
Med.Statin.LLDno 1.333e+00 7.502e-01 0.7553 2.3523
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.261e+00 7.931e-01 0.5777 2.7522
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.925e-01 1.008e+00 0.9802 1.0049
BMI 1.056e+00 9.474e-01 0.9927 1.1223
MedHx_CVDNo 5.322e-01 1.879e+00 0.2979 0.9508
stenose0-49% 1.360e+00 7.351e-01 0.0000 Inf
stenose50-70% 7.222e+06 1.385e-07 0.0000 Inf
stenose70-90% 1.330e+07 7.521e-08 0.0000 Inf
stenose90-99% 1.396e+07 7.163e-08 0.0000 Inf
stenose100% (Occlusion) 2.215e+00 4.515e-01 0.0000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.763 (se = 0.026 )
Likelihood ratio test= 59.24 on 31 df, p=0.002
Wald test = 43.21 on 31 df, p=0.07
Score (logrank) test = 57.21 on 31 df, p=0.003
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epmajor.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epmajor.3years
Protein...................: CD36
Effect size...............: 0.333231
Standard error............: 0.255645
Odds ratio (effect size)..: 1.395
Lower 95% CI..............: 0.845
Upper 95% CI..............: 2.303
T-value...................: 1.303494
P-value...................: 0.1924063
Sample size in model......: 540
Number of events..........: 68
* Analyzing the effect of plaque target-of-interest on [epstroke.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 37
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 3.249e-02 1.033e+00 3.645e-01 0.089 0.9290
Age 5.178e-02 1.053e+00 2.383e-02 2.172 0.0298 *
Gendermale 3.532e-01 1.424e+00 4.374e-01 0.808 0.4194
ORdate_year2002 1.509e+01 3.571e+06 1.440e+04 0.001 0.9992
ORdate_year2003 1.402e+01 1.221e+06 1.440e+04 0.001 0.9992
ORdate_year2004 1.442e+01 1.828e+06 1.440e+04 0.001 0.9992
ORdate_year2005 1.488e+01 2.913e+06 1.440e+04 0.001 0.9992
ORdate_year2006 1.543e+01 5.029e+06 1.440e+04 0.001 0.9991
ORdate_year2007 1.398e+01 1.175e+06 1.440e+04 0.001 0.9992
ORdate_year2008 1.513e+01 3.711e+06 1.440e+04 0.001 0.9992
ORdate_year2009 1.319e+01 5.353e+05 1.440e+04 0.001 0.9993
ORdate_year2010 1.518e+01 3.895e+06 1.440e+04 0.001 0.9992
ORdate_year2011 1.404e+01 1.247e+06 1.440e+04 0.001 0.9992
ORdate_year2012 1.492e+01 3.031e+06 1.440e+04 0.001 0.9992
ORdate_year2013 -1.274e+00 2.797e-01 1.588e+04 0.000 0.9999
ORdate_year2014 -1.171e+00 3.101e-01 2.036e+04 0.000 1.0000
ORdate_year2015 -1.234e+00 2.911e-01 1.627e+04 0.000 0.9999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -3.343e-01 7.158e-01 6.466e-01 -0.517 0.6051
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.469e-01 1.563e+00 3.852e-01 1.160 0.2461
SmokerStatusEx-smoker -6.414e-01 5.266e-01 3.592e-01 -1.785 0.0742 .
SmokerStatusNever smoked -1.681e+00 1.863e-01 7.590e-01 -2.214 0.0268 *
Med.Statin.LLDno 3.053e-01 1.357e+00 3.905e-01 0.782 0.4343
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.504e-01 1.162e+00 5.609e-01 0.268 0.7886
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 7.807e-03 1.008e+00 8.170e-03 0.956 0.3393
BMI 8.859e-02 1.093e+00 4.018e-02 2.205 0.0275 *
MedHx_CVDNo -6.095e-01 5.436e-01 3.947e-01 -1.544 0.1225
stenose0-49% 1.678e+00 5.353e+00 1.596e+04 0.000 0.9999
stenose50-70% 1.727e+01 3.176e+07 1.440e+04 0.001 0.9990
stenose70-90% 1.736e+01 3.472e+07 1.440e+04 0.001 0.9990
stenose90-99% 1.739e+01 3.551e+07 1.440e+04 0.001 0.9990
stenose100% (Occlusion) 1.496e+00 4.462e+00 1.545e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.033e+00 9.680e-01 0.50567 2.1103
Age 1.053e+00 9.495e-01 1.00507 1.1035
Gendermale 1.424e+00 7.024e-01 0.60406 3.3552
ORdate_year2002 3.571e+06 2.800e-07 0.00000 Inf
ORdate_year2003 1.221e+06 8.191e-07 0.00000 Inf
ORdate_year2004 1.828e+06 5.470e-07 0.00000 Inf
ORdate_year2005 2.913e+06 3.433e-07 0.00000 Inf
ORdate_year2006 5.029e+06 1.989e-07 0.00000 Inf
ORdate_year2007 1.175e+06 8.513e-07 0.00000 Inf
ORdate_year2008 3.711e+06 2.695e-07 0.00000 Inf
ORdate_year2009 5.353e+05 1.868e-06 0.00000 Inf
ORdate_year2010 3.895e+06 2.567e-07 0.00000 Inf
ORdate_year2011 1.247e+06 8.017e-07 0.00000 Inf
ORdate_year2012 3.031e+06 3.300e-07 0.00000 Inf
ORdate_year2013 2.797e-01 3.575e+00 0.00000 Inf
ORdate_year2014 3.101e-01 3.225e+00 0.00000 Inf
ORdate_year2015 2.911e-01 3.436e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 7.158e-01 1.397e+00 0.20158 2.5420
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.563e+00 6.396e-01 0.73479 3.3264
SmokerStatusEx-smoker 5.266e-01 1.899e+00 0.26041 1.0647
SmokerStatusNever smoked 1.863e-01 5.369e+00 0.04208 0.8246
Med.Statin.LLDno 1.357e+00 7.369e-01 0.63127 2.9174
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.162e+00 8.604e-01 0.38714 3.4893
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.008e+00 9.922e-01 0.99183 1.0241
BMI 1.093e+00 9.152e-01 1.00989 1.1822
MedHx_CVDNo 5.436e-01 1.840e+00 0.25080 1.1783
stenose0-49% 5.353e+00 1.868e-01 0.00000 Inf
stenose50-70% 3.176e+07 3.149e-08 0.00000 Inf
stenose70-90% 3.472e+07 2.880e-08 0.00000 Inf
stenose90-99% 3.551e+07 2.816e-08 0.00000 Inf
stenose100% (Occlusion) 4.462e+00 2.241e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.779 (se = 0.033 )
Likelihood ratio test= 37.69 on 31 df, p=0.2
Wald test = 27.39 on 31 df, p=0.7
Score (logrank) test = 36.59 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: CXCL10
Effect size...............: 0.03249
Standard error............: 0.364474
Odds ratio (effect size)..: 1.033
Lower 95% CI..............: 0.506
Upper 95% CI..............: 2.11
T-value...................: 0.089142
P-value...................: 0.9289689
Sample size in model......: 540
Number of events..........: 37
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 37
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] -9.524e-01 3.858e-01 4.906e-01 -1.941 0.0522 .
Age 5.111e-02 1.052e+00 2.375e-02 2.152 0.0314 *
Gendermale 3.162e-01 1.372e+00 4.410e-01 0.717 0.4733
ORdate_year2002 1.524e+01 4.148e+06 1.472e+04 0.001 0.9992
ORdate_year2003 1.437e+01 1.745e+06 1.472e+04 0.001 0.9992
ORdate_year2004 1.467e+01 2.348e+06 1.472e+04 0.001 0.9992
ORdate_year2005 1.508e+01 3.535e+06 1.472e+04 0.001 0.9992
ORdate_year2006 1.572e+01 6.701e+06 1.472e+04 0.001 0.9991
ORdate_year2007 1.430e+01 1.630e+06 1.472e+04 0.001 0.9992
ORdate_year2008 1.538e+01 4.761e+06 1.472e+04 0.001 0.9992
ORdate_year2009 1.350e+01 7.330e+05 1.472e+04 0.001 0.9993
ORdate_year2010 1.556e+01 5.702e+06 1.472e+04 0.001 0.9992
ORdate_year2011 1.434e+01 1.690e+06 1.472e+04 0.001 0.9992
ORdate_year2012 1.526e+01 4.250e+06 1.472e+04 0.001 0.9992
ORdate_year2013 -9.912e-01 3.711e-01 1.620e+04 0.000 1.0000
ORdate_year2014 -1.102e+00 3.323e-01 2.081e+04 0.000 1.0000
ORdate_year2015 -9.365e-01 3.920e-01 1.673e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -3.261e-01 7.217e-01 6.485e-01 -0.503 0.6151
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.721e-01 1.603e+00 3.847e-01 1.227 0.2197
SmokerStatusEx-smoker -6.149e-01 5.407e-01 3.628e-01 -1.695 0.0901 .
SmokerStatusNever smoked -1.726e+00 1.780e-01 7.589e-01 -2.275 0.0229 *
Med.Statin.LLDno 3.104e-01 1.364e+00 3.917e-01 0.792 0.4281
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.365e-02 1.024e+00 5.679e-01 0.042 0.9668
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 8.189e-03 1.008e+00 8.203e-03 0.998 0.3181
BMI 9.501e-02 1.100e+00 4.145e-02 2.292 0.0219 *
MedHx_CVDNo -6.016e-01 5.479e-01 3.971e-01 -1.515 0.1298
stenose0-49% 2.418e+00 1.122e+01 1.688e+04 0.000 0.9999
stenose50-70% 1.763e+01 4.524e+07 1.472e+04 0.001 0.9990
stenose70-90% 1.767e+01 4.730e+07 1.472e+04 0.001 0.9990
stenose90-99% 1.773e+01 5.019e+07 1.472e+04 0.001 0.9990
stenose100% (Occlusion) 1.861e+00 6.431e+00 1.562e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 3.858e-01 2.592e+00 0.14748 1.0092
Age 1.052e+00 9.502e-01 1.00457 1.1026
Gendermale 1.372e+00 7.289e-01 0.57805 3.2563
ORdate_year2002 4.148e+06 2.411e-07 0.00000 Inf
ORdate_year2003 1.745e+06 5.729e-07 0.00000 Inf
ORdate_year2004 2.348e+06 4.259e-07 0.00000 Inf
ORdate_year2005 3.535e+06 2.829e-07 0.00000 Inf
ORdate_year2006 6.701e+06 1.492e-07 0.00000 Inf
ORdate_year2007 1.630e+06 6.136e-07 0.00000 Inf
ORdate_year2008 4.761e+06 2.100e-07 0.00000 Inf
ORdate_year2009 7.330e+05 1.364e-06 0.00000 Inf
ORdate_year2010 5.702e+06 1.754e-07 0.00000 Inf
ORdate_year2011 1.690e+06 5.917e-07 0.00000 Inf
ORdate_year2012 4.250e+06 2.353e-07 0.00000 Inf
ORdate_year2013 3.711e-01 2.695e+00 0.00000 Inf
ORdate_year2014 3.323e-01 3.009e+00 0.00000 Inf
ORdate_year2015 3.920e-01 2.551e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 7.217e-01 1.386e+00 0.20247 2.5726
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.603e+00 6.237e-01 0.75441 3.4078
SmokerStatusEx-smoker 5.407e-01 1.849e+00 0.26557 1.1009
SmokerStatusNever smoked 1.780e-01 5.619e+00 0.04022 0.7876
Med.Statin.LLDno 1.364e+00 7.331e-01 0.63295 2.9395
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.024e+00 9.766e-01 0.33639 3.1167
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.008e+00 9.918e-01 0.99214 1.0246
BMI 1.100e+00 9.094e-01 1.01386 1.1927
MedHx_CVDNo 5.479e-01 1.825e+00 0.25160 1.1933
stenose0-49% 1.122e+01 8.913e-02 0.00000 Inf
stenose50-70% 4.524e+07 2.210e-08 0.00000 Inf
stenose70-90% 4.730e+07 2.114e-08 0.00000 Inf
stenose90-99% 5.019e+07 1.992e-08 0.00000 Inf
stenose100% (Occlusion) 6.431e+00 1.555e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.79 (se = 0.032 )
Likelihood ratio test= 42.29 on 31 df, p=0.09
Wald test = 29.8 on 31 df, p=0.5
Score (logrank) test = 40.27 on 31 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: PCSK9
Effect size...............: -0.952429
Standard error............: 0.490637
Odds ratio (effect size)..: 0.386
Lower 95% CI..............: 0.147
Upper 95% CI..............: 1.009
T-value...................: -1.941208
P-value...................: 0.05223304
Sample size in model......: 540
Number of events..........: 37
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 37
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 3.548e-01 1.426e+00 3.449e-01 1.029 0.3036
Age 5.210e-02 1.053e+00 2.362e-02 2.206 0.0274 *
Gendermale 3.440e-01 1.411e+00 4.384e-01 0.785 0.4326
ORdate_year2002 1.533e+01 4.562e+06 1.448e+04 0.001 0.9992
ORdate_year2003 1.418e+01 1.436e+06 1.448e+04 0.001 0.9992
ORdate_year2004 1.454e+01 2.062e+06 1.448e+04 0.001 0.9992
ORdate_year2005 1.503e+01 3.364e+06 1.448e+04 0.001 0.9992
ORdate_year2006 1.559e+01 5.912e+06 1.448e+04 0.001 0.9991
ORdate_year2007 1.413e+01 1.368e+06 1.448e+04 0.001 0.9992
ORdate_year2008 1.531e+01 4.435e+06 1.448e+04 0.001 0.9992
ORdate_year2009 1.326e+01 5.728e+05 1.448e+04 0.001 0.9993
ORdate_year2010 1.527e+01 4.280e+06 1.448e+04 0.001 0.9992
ORdate_year2011 1.424e+01 1.534e+06 1.448e+04 0.001 0.9992
ORdate_year2012 1.509e+01 3.582e+06 1.448e+04 0.001 0.9992
ORdate_year2013 -1.150e+00 3.166e-01 1.600e+04 0.000 0.9999
ORdate_year2014 -1.172e+00 3.097e-01 2.047e+04 0.000 1.0000
ORdate_year2015 -1.175e+00 3.088e-01 1.623e+04 0.000 0.9999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -3.242e-01 7.231e-01 6.470e-01 -0.501 0.6163
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.272e-01 1.533e+00 3.862e-01 1.106 0.2687
SmokerStatusEx-smoker -6.468e-01 5.237e-01 3.602e-01 -1.796 0.0725 .
SmokerStatusNever smoked -1.723e+00 1.785e-01 7.629e-01 -2.259 0.0239 *
Med.Statin.LLDno 3.169e-01 1.373e+00 3.900e-01 0.812 0.4165
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.879e-01 1.207e+00 5.606e-01 0.335 0.7375
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 7.279e-03 1.007e+00 8.198e-03 0.888 0.3746
BMI 9.014e-02 1.094e+00 3.984e-02 2.263 0.0237 *
MedHx_CVDNo -6.327e-01 5.312e-01 3.957e-01 -1.599 0.1098
stenose0-49% 1.525e+00 4.593e+00 1.623e+04 0.000 0.9999
stenose50-70% 1.711e+01 2.706e+07 1.448e+04 0.001 0.9991
stenose70-90% 1.718e+01 2.902e+07 1.448e+04 0.001 0.9991
stenose90-99% 1.722e+01 3.018e+07 1.448e+04 0.001 0.9991
stenose100% (Occlusion) 1.323e+00 3.753e+00 1.564e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.426e+00 7.013e-01 0.72530 2.803
Age 1.053e+00 9.492e-01 1.00583 1.103
Gendermale 1.411e+00 7.089e-01 0.59742 3.331
ORdate_year2002 4.562e+06 2.192e-07 0.00000 Inf
ORdate_year2003 1.436e+06 6.964e-07 0.00000 Inf
ORdate_year2004 2.062e+06 4.851e-07 0.00000 Inf
ORdate_year2005 3.364e+06 2.973e-07 0.00000 Inf
ORdate_year2006 5.912e+06 1.691e-07 0.00000 Inf
ORdate_year2007 1.368e+06 7.312e-07 0.00000 Inf
ORdate_year2008 4.435e+06 2.255e-07 0.00000 Inf
ORdate_year2009 5.728e+05 1.746e-06 0.00000 Inf
ORdate_year2010 4.280e+06 2.337e-07 0.00000 Inf
ORdate_year2011 1.534e+06 6.517e-07 0.00000 Inf
ORdate_year2012 3.582e+06 2.791e-07 0.00000 Inf
ORdate_year2013 3.166e-01 3.158e+00 0.00000 Inf
ORdate_year2014 3.097e-01 3.229e+00 0.00000 Inf
ORdate_year2015 3.088e-01 3.239e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 7.231e-01 1.383e+00 0.20344 2.570
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.533e+00 6.524e-01 0.71905 3.268
SmokerStatusEx-smoker 5.237e-01 1.909e+00 0.25855 1.061
SmokerStatusNever smoked 1.785e-01 5.604e+00 0.04001 0.796
Med.Statin.LLDno 1.373e+00 7.284e-01 0.63918 2.949
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.207e+00 8.287e-01 0.40221 3.620
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.007e+00 9.927e-01 0.99125 1.024
BMI 1.094e+00 9.138e-01 1.01213 1.183
MedHx_CVDNo 5.312e-01 1.883e+00 0.24459 1.153
stenose0-49% 4.593e+00 2.177e-01 0.00000 Inf
stenose50-70% 2.706e+07 3.696e-08 0.00000 Inf
stenose70-90% 2.902e+07 3.446e-08 0.00000 Inf
stenose90-99% 3.018e+07 3.314e-08 0.00000 Inf
stenose100% (Occlusion) 3.753e+00 2.664e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.782 (se = 0.035 )
Likelihood ratio test= 38.75 on 31 df, p=0.2
Wald test = 28.55 on 31 df, p=0.6
Score (logrank) test = 37.5 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: COL4A1
Effect size...............: 0.35478
Standard error............: 0.344876
Odds ratio (effect size)..: 1.426
Lower 95% CI..............: 0.725
Upper 95% CI..............: 2.803
T-value...................: 1.028717
P-value...................: 0.3036127
Sample size in model......: 540
Number of events..........: 37
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 37
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 2.701e-01 1.310e+00 3.458e-01 0.781 0.4348
Age 5.171e-02 1.053e+00 2.375e-02 2.177 0.0295 *
Gendermale 3.691e-01 1.446e+00 4.388e-01 0.841 0.4002
ORdate_year2002 1.526e+01 4.249e+06 1.448e+04 0.001 0.9992
ORdate_year2003 1.414e+01 1.389e+06 1.448e+04 0.001 0.9992
ORdate_year2004 1.454e+01 2.055e+06 1.448e+04 0.001 0.9992
ORdate_year2005 1.498e+01 3.209e+06 1.448e+04 0.001 0.9992
ORdate_year2006 1.556e+01 5.698e+06 1.448e+04 0.001 0.9991
ORdate_year2007 1.417e+01 1.424e+06 1.448e+04 0.001 0.9992
ORdate_year2008 1.529e+01 4.366e+06 1.448e+04 0.001 0.9992
ORdate_year2009 1.327e+01 5.784e+05 1.448e+04 0.001 0.9993
ORdate_year2010 1.528e+01 4.337e+06 1.448e+04 0.001 0.9992
ORdate_year2011 1.418e+01 1.440e+06 1.448e+04 0.001 0.9992
ORdate_year2012 1.508e+01 3.549e+06 1.448e+04 0.001 0.9992
ORdate_year2013 -1.125e+00 3.247e-01 1.603e+04 0.000 0.9999
ORdate_year2014 -1.168e+00 3.111e-01 2.047e+04 0.000 1.0000
ORdate_year2015 -1.154e+00 3.154e-01 1.624e+04 0.000 0.9999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -3.244e-01 7.229e-01 6.444e-01 -0.503 0.6146
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.454e-01 1.561e+00 3.874e-01 1.150 0.2502
SmokerStatusEx-smoker -6.459e-01 5.242e-01 3.596e-01 -1.796 0.0724 .
SmokerStatusNever smoked -1.698e+00 1.831e-01 7.605e-01 -2.232 0.0256 *
Med.Statin.LLDno 3.193e-01 1.376e+00 3.902e-01 0.818 0.4131
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.572e-01 1.170e+00 5.600e-01 0.281 0.7790
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 7.107e-03 1.007e+00 8.158e-03 0.871 0.3837
BMI 9.145e-02 1.096e+00 4.015e-02 2.278 0.0227 *
MedHx_CVDNo -6.034e-01 5.469e-01 3.951e-01 -1.527 0.1267
stenose0-49% 1.584e+00 4.874e+00 1.621e+04 0.000 0.9999
stenose50-70% 1.712e+01 2.731e+07 1.448e+04 0.001 0.9991
stenose70-90% 1.726e+01 3.128e+07 1.448e+04 0.001 0.9990
stenose90-99% 1.727e+01 3.177e+07 1.448e+04 0.001 0.9990
stenose100% (Occlusion) 1.387e+00 4.002e+00 1.563e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.310e+00 7.633e-01 0.66522 2.5800
Age 1.053e+00 9.496e-01 1.00517 1.1033
Gendermale 1.446e+00 6.913e-01 0.61208 3.4183
ORdate_year2002 4.249e+06 2.353e-07 0.00000 Inf
ORdate_year2003 1.389e+06 7.199e-07 0.00000 Inf
ORdate_year2004 2.055e+06 4.866e-07 0.00000 Inf
ORdate_year2005 3.209e+06 3.117e-07 0.00000 Inf
ORdate_year2006 5.698e+06 1.755e-07 0.00000 Inf
ORdate_year2007 1.424e+06 7.022e-07 0.00000 Inf
ORdate_year2008 4.366e+06 2.290e-07 0.00000 Inf
ORdate_year2009 5.784e+05 1.729e-06 0.00000 Inf
ORdate_year2010 4.337e+06 2.306e-07 0.00000 Inf
ORdate_year2011 1.440e+06 6.945e-07 0.00000 Inf
ORdate_year2012 3.549e+06 2.818e-07 0.00000 Inf
ORdate_year2013 3.247e-01 3.079e+00 0.00000 Inf
ORdate_year2014 3.111e-01 3.214e+00 0.00000 Inf
ORdate_year2015 3.154e-01 3.171e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 7.229e-01 1.383e+00 0.20444 2.5564
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.561e+00 6.406e-01 0.73061 3.3358
SmokerStatusEx-smoker 5.242e-01 1.908e+00 0.25907 1.0606
SmokerStatusNever smoked 1.831e-01 5.462e+00 0.04124 0.8129
Med.Statin.LLDno 1.376e+00 7.266e-01 0.64056 2.9566
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.170e+00 8.546e-01 0.39044 3.5071
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.007e+00 9.929e-01 0.99116 1.0234
BMI 1.096e+00 9.126e-01 1.01284 1.1855
MedHx_CVDNo 5.469e-01 1.828e+00 0.25214 1.1864
stenose0-49% 4.874e+00 2.052e-01 0.00000 Inf
stenose50-70% 2.731e+07 3.662e-08 0.00000 Inf
stenose70-90% 3.128e+07 3.197e-08 0.00000 Inf
stenose90-99% 3.177e+07 3.147e-08 0.00000 Inf
stenose100% (Occlusion) 4.002e+00 2.499e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.782 (se = 0.033 )
Likelihood ratio test= 38.29 on 31 df, p=0.2
Wald test = 28.08 on 31 df, p=0.6
Score (logrank) test = 37.23 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: COL4A2
Effect size...............: 0.27007
Standard error............: 0.345778
Odds ratio (effect size)..: 1.31
Lower 95% CI..............: 0.665
Upper 95% CI..............: 2.58
T-value...................: 0.781051
P-value...................: 0.4347723
Sample size in model......: 540
Number of events..........: 37
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 37
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -2.218e-01 8.011e-01 3.484e-01 -0.637 0.5243
Age 5.216e-02 1.054e+00 2.386e-02 2.186 0.0288 *
Gendermale 3.586e-01 1.431e+00 4.374e-01 0.820 0.4122
ORdate_year2002 1.523e+01 4.124e+06 1.453e+04 0.001 0.9992
ORdate_year2003 1.421e+01 1.482e+06 1.453e+04 0.001 0.9992
ORdate_year2004 1.457e+01 2.126e+06 1.453e+04 0.001 0.9992
ORdate_year2005 1.507e+01 3.514e+06 1.453e+04 0.001 0.9992
ORdate_year2006 1.563e+01 6.111e+06 1.453e+04 0.001 0.9991
ORdate_year2007 1.416e+01 1.407e+06 1.453e+04 0.001 0.9992
ORdate_year2008 1.529e+01 4.389e+06 1.453e+04 0.001 0.9992
ORdate_year2009 1.343e+01 6.801e+05 1.453e+04 0.001 0.9993
ORdate_year2010 1.537e+01 4.730e+06 1.453e+04 0.001 0.9992
ORdate_year2011 1.419e+01 1.455e+06 1.453e+04 0.001 0.9992
ORdate_year2012 1.507e+01 3.489e+06 1.453e+04 0.001 0.9992
ORdate_year2013 -1.090e+00 3.362e-01 1.598e+04 0.000 0.9999
ORdate_year2014 -8.752e-01 4.168e-01 2.054e+04 0.000 1.0000
ORdate_year2015 -1.031e+00 3.567e-01 1.650e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -3.548e-01 7.013e-01 6.468e-01 -0.548 0.5834
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.450e-01 1.560e+00 3.856e-01 1.154 0.2485
SmokerStatusEx-smoker -6.443e-01 5.250e-01 3.589e-01 -1.796 0.0726 .
SmokerStatusNever smoked -1.656e+00 1.910e-01 7.603e-01 -2.178 0.0294 *
Med.Statin.LLDno 2.915e-01 1.338e+00 3.910e-01 0.746 0.4559
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.019e-01 1.107e+00 5.684e-01 0.179 0.8578
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 7.859e-03 1.008e+00 8.160e-03 0.963 0.3355
BMI 8.912e-02 1.093e+00 4.036e-02 2.208 0.0272 *
MedHx_CVDNo -5.745e-01 5.630e-01 3.981e-01 -1.443 0.1489
stenose0-49% 1.840e+00 6.295e+00 1.609e+04 0.000 0.9999
stenose50-70% 1.737e+01 3.512e+07 1.453e+04 0.001 0.9990
stenose70-90% 1.744e+01 3.756e+07 1.453e+04 0.001 0.9990
stenose90-99% 1.745e+01 3.779e+07 1.453e+04 0.001 0.9990
stenose100% (Occlusion) 1.625e+00 5.076e+00 1.545e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 8.011e-01 1.248e+00 0.40465 1.5858
Age 1.054e+00 9.492e-01 1.00541 1.1040
Gendermale 1.431e+00 6.986e-01 0.60739 3.3731
ORdate_year2002 4.124e+06 2.425e-07 0.00000 Inf
ORdate_year2003 1.482e+06 6.747e-07 0.00000 Inf
ORdate_year2004 2.126e+06 4.704e-07 0.00000 Inf
ORdate_year2005 3.514e+06 2.846e-07 0.00000 Inf
ORdate_year2006 6.111e+06 1.637e-07 0.00000 Inf
ORdate_year2007 1.407e+06 7.109e-07 0.00000 Inf
ORdate_year2008 4.389e+06 2.278e-07 0.00000 Inf
ORdate_year2009 6.801e+05 1.470e-06 0.00000 Inf
ORdate_year2010 4.730e+06 2.114e-07 0.00000 Inf
ORdate_year2011 1.455e+06 6.872e-07 0.00000 Inf
ORdate_year2012 3.489e+06 2.866e-07 0.00000 Inf
ORdate_year2013 3.362e-01 2.975e+00 0.00000 Inf
ORdate_year2014 4.168e-01 2.399e+00 0.00000 Inf
ORdate_year2015 3.567e-01 2.804e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 7.013e-01 1.426e+00 0.19741 2.4917
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.560e+00 6.408e-01 0.73291 3.3223
SmokerStatusEx-smoker 5.250e-01 1.905e+00 0.25984 1.0608
SmokerStatusNever smoked 1.910e-01 5.236e+00 0.04304 0.8475
Med.Statin.LLDno 1.338e+00 7.471e-01 0.62200 2.8803
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.107e+00 9.031e-01 0.36345 3.3732
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.008e+00 9.922e-01 0.99190 1.0241
BMI 1.093e+00 9.147e-01 1.01008 1.1832
MedHx_CVDNo 5.630e-01 1.776e+00 0.25802 1.2283
stenose0-49% 6.295e+00 1.589e-01 0.00000 Inf
stenose50-70% 3.512e+07 2.847e-08 0.00000 Inf
stenose70-90% 3.756e+07 2.662e-08 0.00000 Inf
stenose90-99% 3.779e+07 2.646e-08 0.00000 Inf
stenose100% (Occlusion) 5.076e+00 1.970e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.783 (se = 0.032 )
Likelihood ratio test= 38.09 on 31 df, p=0.2
Wald test = 27.75 on 31 df, p=0.6
Score (logrank) test = 37.33 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: LDLR
Effect size...............: -0.221831
Standard error............: 0.348429
Odds ratio (effect size)..: 0.801
Lower 95% CI..............: 0.405
Upper 95% CI..............: 1.586
T-value...................: -0.636661
P-value...................: 0.5243457
Sample size in model......: 540
Number of events..........: 37
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 37
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 3.862e-01 1.471e+00 3.459e-01 1.117 0.2642
Age 5.141e-02 1.053e+00 2.349e-02 2.189 0.0286 *
Gendermale 3.558e-01 1.427e+00 4.369e-01 0.814 0.4154
ORdate_year2002 1.538e+01 4.788e+06 1.447e+04 0.001 0.9992
ORdate_year2003 1.424e+01 1.522e+06 1.447e+04 0.001 0.9992
ORdate_year2004 1.463e+01 2.256e+06 1.447e+04 0.001 0.9992
ORdate_year2005 1.506e+01 3.481e+06 1.447e+04 0.001 0.9992
ORdate_year2006 1.561e+01 5.990e+06 1.447e+04 0.001 0.9991
ORdate_year2007 1.419e+01 1.456e+06 1.447e+04 0.001 0.9992
ORdate_year2008 1.535e+01 4.627e+06 1.447e+04 0.001 0.9992
ORdate_year2009 1.330e+01 5.980e+05 1.447e+04 0.001 0.9993
ORdate_year2010 1.536e+01 4.707e+06 1.447e+04 0.001 0.9992
ORdate_year2011 1.424e+01 1.525e+06 1.447e+04 0.001 0.9992
ORdate_year2012 1.509e+01 3.578e+06 1.447e+04 0.001 0.9992
ORdate_year2013 -8.707e-01 4.187e-01 1.597e+04 0.000 1.0000
ORdate_year2014 -1.140e+00 3.199e-01 2.047e+04 0.000 1.0000
ORdate_year2015 -1.162e+00 3.128e-01 1.621e+04 0.000 0.9999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -3.263e-01 7.216e-01 6.478e-01 -0.504 0.6145
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 4.709e-01 1.601e+00 3.863e-01 1.219 0.2229
SmokerStatusEx-smoker -6.638e-01 5.149e-01 3.595e-01 -1.846 0.0648 .
SmokerStatusNever smoked -1.678e+00 1.867e-01 7.607e-01 -2.206 0.0274 *
Med.Statin.LLDno 2.964e-01 1.345e+00 3.889e-01 0.762 0.4460
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 2.176e-01 1.243e+00 5.627e-01 0.387 0.6990
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD 7.257e-03 1.007e+00 8.272e-03 0.877 0.3803
BMI 8.821e-02 1.092e+00 4.011e-02 2.199 0.0278 *
MedHx_CVDNo -6.283e-01 5.335e-01 3.951e-01 -1.590 0.1118
stenose0-49% 1.505e+00 4.502e+00 1.628e+04 0.000 0.9999
stenose50-70% 1.709e+01 2.656e+07 1.447e+04 0.001 0.9991
stenose70-90% 1.715e+01 2.800e+07 1.447e+04 0.001 0.9991
stenose90-99% 1.720e+01 2.963e+07 1.447e+04 0.001 0.9991
stenose100% (Occlusion) 1.287e+00 3.621e+00 1.567e+04 0.000 0.9999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.471e+00 6.797e-01 0.74698 2.8980
Age 1.053e+00 9.499e-01 1.00539 1.1024
Gendermale 1.427e+00 7.006e-01 0.60620 3.3610
ORdate_year2002 4.788e+06 2.088e-07 0.00000 Inf
ORdate_year2003 1.522e+06 6.570e-07 0.00000 Inf
ORdate_year2004 2.256e+06 4.433e-07 0.00000 Inf
ORdate_year2005 3.481e+06 2.872e-07 0.00000 Inf
ORdate_year2006 5.990e+06 1.669e-07 0.00000 Inf
ORdate_year2007 1.456e+06 6.870e-07 0.00000 Inf
ORdate_year2008 4.627e+06 2.161e-07 0.00000 Inf
ORdate_year2009 5.980e+05 1.672e-06 0.00000 Inf
ORdate_year2010 4.707e+06 2.125e-07 0.00000 Inf
ORdate_year2011 1.525e+06 6.556e-07 0.00000 Inf
ORdate_year2012 3.578e+06 2.795e-07 0.00000 Inf
ORdate_year2013 4.187e-01 2.389e+00 0.00000 Inf
ORdate_year2014 3.199e-01 3.126e+00 0.00000 Inf
ORdate_year2015 3.128e-01 3.197e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 7.216e-01 1.386e+00 0.20272 2.5687
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.601e+00 6.245e-01 0.75104 3.4144
SmokerStatusEx-smoker 5.149e-01 1.942e+00 0.25451 1.0417
SmokerStatusNever smoked 1.867e-01 5.357e+00 0.04203 0.8291
Med.Statin.LLDno 1.345e+00 7.435e-01 0.62763 2.8821
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 1.243e+00 8.044e-01 0.41257 3.7456
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 1.007e+00 9.928e-01 0.99108 1.0237
BMI 1.092e+00 9.156e-01 1.00965 1.1815
MedHx_CVDNo 5.335e-01 1.874e+00 0.24592 1.1573
stenose0-49% 4.502e+00 2.221e-01 0.00000 Inf
stenose50-70% 2.656e+07 3.765e-08 0.00000 Inf
stenose70-90% 2.800e+07 3.571e-08 0.00000 Inf
stenose90-99% 2.963e+07 3.375e-08 0.00000 Inf
stenose100% (Occlusion) 3.621e+00 2.761e-01 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.779 (se = 0.036 )
Likelihood ratio test= 38.94 on 31 df, p=0.2
Wald test = 28.52 on 31 df, p=0.6
Score (logrank) test = 37.68 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epstroke.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epstroke.3years
Protein...................: CD36
Effect size...............: 0.386153
Standard error............: 0.34586
Odds ratio (effect size)..: 1.471
Lower 95% CI..............: 0.747
Upper 95% CI..............: 2.898
T-value...................: 1.116502
P-value...................: 0.2642075
Sample size in model......: 540
Number of events..........: 37
* Analyzing the effect of plaque target-of-interest on [epcoronary.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 43
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 4.292e-01 1.536e+00 3.195e-01 1.344 0.17905
Age 1.453e-03 1.001e+00 2.299e-02 0.063 0.94960
Gendermale 1.287e+00 3.620e+00 5.396e-01 2.384 0.01711 *
ORdate_year2002 1.637e+01 1.289e+07 2.125e+04 0.001 0.99939
ORdate_year2003 1.597e+01 8.649e+06 2.125e+04 0.001 0.99940
ORdate_year2004 1.499e+01 3.253e+06 2.125e+04 0.001 0.99944
ORdate_year2005 1.604e+01 9.294e+06 2.125e+04 0.001 0.99940
ORdate_year2006 1.585e+01 7.636e+06 2.125e+04 0.001 0.99940
ORdate_year2007 1.564e+01 6.207e+06 2.125e+04 0.001 0.99941
ORdate_year2008 1.582e+01 7.441e+06 2.125e+04 0.001 0.99941
ORdate_year2009 1.535e+01 4.638e+06 2.125e+04 0.001 0.99942
ORdate_year2010 1.437e+01 1.746e+06 2.125e+04 0.001 0.99946
ORdate_year2011 1.462e+01 2.237e+06 2.125e+04 0.001 0.99945
ORdate_year2012 1.540e+01 4.860e+06 2.125e+04 0.001 0.99942
ORdate_year2013 -1.886e+00 1.517e-01 2.346e+04 0.000 0.99994
ORdate_year2014 -1.154e+00 3.153e-01 3.005e+04 0.000 0.99997
ORdate_year2015 -1.768e-01 8.380e-01 2.317e+04 0.000 0.99999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.667e+00 1.888e-01 1.030e+00 -1.619 0.10544
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.454e-01 1.278e+00 3.487e-01 0.704 0.48157
SmokerStatusEx-smoker -2.921e-01 7.467e-01 3.530e-01 -0.827 0.40797
SmokerStatusNever smoked 1.291e-01 1.138e+00 4.737e-01 0.272 0.78524
Med.Statin.LLDno -3.729e-01 6.887e-01 4.391e-01 -0.849 0.39567
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -3.588e-02 9.648e-01 5.647e-01 -0.064 0.94934
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.531e-02 9.848e-01 8.300e-03 -1.845 0.06505 .
BMI 1.958e-02 1.020e+00 4.226e-02 0.463 0.64315
MedHx_CVDNo -1.204e+00 2.999e-01 4.276e-01 -2.816 0.00486 **
stenose0-49% -2.006e+00 1.345e-01 2.484e+04 0.000 0.99994
stenose50-70% -8.415e-01 4.310e-01 2.151e+04 0.000 0.99997
stenose70-90% 1.601e+01 9.003e+06 2.125e+04 0.001 0.99940
stenose90-99% 1.583e+01 7.490e+06 2.125e+04 0.001 0.99941
stenose100% (Occlusion) -7.449e-01 4.748e-01 2.329e+04 0.000 0.99997
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.536e+00 6.510e-01 0.82130 2.8730
Age 1.001e+00 9.985e-01 0.95733 1.0476
Gendermale 3.620e+00 2.762e-01 1.25729 10.4241
ORdate_year2002 1.289e+07 7.758e-08 0.00000 Inf
ORdate_year2003 8.649e+06 1.156e-07 0.00000 Inf
ORdate_year2004 3.253e+06 3.075e-07 0.00000 Inf
ORdate_year2005 9.294e+06 1.076e-07 0.00000 Inf
ORdate_year2006 7.636e+06 1.310e-07 0.00000 Inf
ORdate_year2007 6.207e+06 1.611e-07 0.00000 Inf
ORdate_year2008 7.441e+06 1.344e-07 0.00000 Inf
ORdate_year2009 4.638e+06 2.156e-07 0.00000 Inf
ORdate_year2010 1.746e+06 5.728e-07 0.00000 Inf
ORdate_year2011 2.237e+06 4.470e-07 0.00000 Inf
ORdate_year2012 4.860e+06 2.058e-07 0.00000 Inf
ORdate_year2013 1.517e-01 6.590e+00 0.00000 Inf
ORdate_year2014 3.153e-01 3.171e+00 0.00000 Inf
ORdate_year2015 8.380e-01 1.193e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 1.888e-01 5.298e+00 0.02508 1.4206
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.278e+00 7.824e-01 0.64532 2.5315
SmokerStatusEx-smoker 7.467e-01 1.339e+00 0.37386 1.4914
SmokerStatusNever smoked 1.138e+00 8.789e-01 0.44965 2.8789
Med.Statin.LLDno 6.887e-01 1.452e+00 0.29127 1.6284
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.648e-01 1.037e+00 0.31897 2.9180
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.848e-01 1.015e+00 0.96891 1.0010
BMI 1.020e+00 9.806e-01 0.93871 1.1078
MedHx_CVDNo 2.999e-01 3.334e+00 0.12971 0.6934
stenose0-49% 1.345e-01 7.435e+00 0.00000 Inf
stenose50-70% 4.310e-01 2.320e+00 0.00000 Inf
stenose70-90% 9.003e+06 1.111e-07 0.00000 Inf
stenose90-99% 7.490e+06 1.335e-07 0.00000 Inf
stenose100% (Occlusion) 4.748e-01 2.106e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.782 (se = 0.028 )
Likelihood ratio test= 47.41 on 31 df, p=0.03
Wald test = 25.79 on 31 df, p=0.7
Score (logrank) test = 39.86 on 31 df, p=0.1
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: CXCL10
Effect size...............: 0.429241
Standard error............: 0.319451
Odds ratio (effect size)..: 1.536
Lower 95% CI..............: 0.821
Upper 95% CI..............: 2.873
T-value...................: 1.343683
P-value...................: 0.1790508
Sample size in model......: 540
Number of events..........: 43
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 43
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] -1.965e-01 8.216e-01 3.766e-01 -0.522 0.60184
Age -4.344e-04 9.996e-01 2.276e-02 -0.019 0.98477
Gendermale 1.290e+00 3.633e+00 5.400e-01 2.389 0.01690 *
ORdate_year2002 1.745e+01 3.784e+07 3.461e+04 0.001 0.99960
ORdate_year2003 1.710e+01 2.670e+07 3.461e+04 0.000 0.99961
ORdate_year2004 1.621e+01 1.098e+07 3.461e+04 0.000 0.99963
ORdate_year2005 1.719e+01 2.933e+07 3.461e+04 0.000 0.99960
ORdate_year2006 1.699e+01 2.397e+07 3.461e+04 0.000 0.99961
ORdate_year2007 1.682e+01 2.025e+07 3.461e+04 0.000 0.99961
ORdate_year2008 1.694e+01 2.266e+07 3.461e+04 0.000 0.99961
ORdate_year2009 1.656e+01 1.563e+07 3.461e+04 0.000 0.99962
ORdate_year2010 1.556e+01 5.725e+06 3.461e+04 0.000 0.99964
ORdate_year2011 1.584e+01 7.556e+06 3.461e+04 0.000 0.99963
ORdate_year2012 1.656e+01 1.552e+07 3.461e+04 0.000 0.99962
ORdate_year2013 -1.761e+00 1.719e-01 3.792e+04 0.000 0.99996
ORdate_year2014 -7.709e-01 4.626e-01 4.894e+04 0.000 0.99999
ORdate_year2015 -7.765e-02 9.253e-01 3.783e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.704e+00 1.819e-01 1.030e+00 -1.655 0.09789 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.781e-01 1.321e+00 3.484e-01 0.798 0.42477
SmokerStatusEx-smoker -2.757e-01 7.591e-01 3.556e-01 -0.775 0.43818
SmokerStatusNever smoked 1.014e-01 1.107e+00 4.724e-01 0.215 0.83000
Med.Statin.LLDno -3.609e-01 6.971e-01 4.365e-01 -0.827 0.40834
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -7.010e-02 9.323e-01 5.632e-01 -0.124 0.90094
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.479e-02 9.853e-01 8.200e-03 -1.803 0.07137 .
BMI 2.059e-02 1.021e+00 4.211e-02 0.489 0.62486
MedHx_CVDNo -1.191e+00 3.038e-01 4.256e-01 -2.799 0.00512 **
stenose0-49% -1.676e+00 1.872e-01 3.995e+04 0.000 0.99997
stenose50-70% -7.068e-01 4.932e-01 3.502e+04 0.000 0.99998
stenose70-90% 1.721e+01 2.986e+07 3.461e+04 0.000 0.99960
stenose90-99% 1.701e+01 2.445e+07 3.461e+04 0.000 0.99961
stenose100% (Occlusion) -4.881e-01 6.138e-01 3.747e+04 0.000 0.99999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 8.216e-01 1.217e+00 0.39273 1.7188
Age 9.996e-01 1.000e+00 0.95595 1.0452
Gendermale 3.633e+00 2.753e-01 1.26068 10.4693
ORdate_year2002 3.784e+07 2.643e-08 0.00000 Inf
ORdate_year2003 2.670e+07 3.746e-08 0.00000 Inf
ORdate_year2004 1.098e+07 9.108e-08 0.00000 Inf
ORdate_year2005 2.933e+07 3.410e-08 0.00000 Inf
ORdate_year2006 2.397e+07 4.171e-08 0.00000 Inf
ORdate_year2007 2.025e+07 4.939e-08 0.00000 Inf
ORdate_year2008 2.266e+07 4.414e-08 0.00000 Inf
ORdate_year2009 1.563e+07 6.399e-08 0.00000 Inf
ORdate_year2010 5.725e+06 1.747e-07 0.00000 Inf
ORdate_year2011 7.556e+06 1.323e-07 0.00000 Inf
ORdate_year2012 1.552e+07 6.442e-08 0.00000 Inf
ORdate_year2013 1.719e-01 5.818e+00 0.00000 Inf
ORdate_year2014 4.626e-01 2.162e+00 0.00000 Inf
ORdate_year2015 9.253e-01 1.081e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 1.819e-01 5.496e+00 0.02419 1.3686
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.321e+00 7.573e-01 0.66716 2.6139
SmokerStatusEx-smoker 7.591e-01 1.317e+00 0.37809 1.5239
SmokerStatusNever smoked 1.107e+00 9.035e-01 0.43844 2.7938
Med.Statin.LLDno 6.971e-01 1.435e+00 0.29632 1.6398
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.323e-01 1.073e+00 0.30917 2.8113
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.853e-01 1.015e+00 0.96961 1.0013
BMI 1.021e+00 9.796e-01 0.93993 1.1086
MedHx_CVDNo 3.038e-01 3.291e+00 0.13195 0.6997
stenose0-49% 1.872e-01 5.342e+00 0.00000 Inf
stenose50-70% 4.932e-01 2.028e+00 0.00000 Inf
stenose70-90% 2.986e+07 3.349e-08 0.00000 Inf
stenose90-99% 2.445e+07 4.089e-08 0.00000 Inf
stenose100% (Occlusion) 6.138e-01 1.629e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.779 (se = 0.028 )
Likelihood ratio test= 45.94 on 31 df, p=0.04
Wald test = 24.3 on 31 df, p=0.8
Score (logrank) test = 37.79 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: PCSK9
Effect size...............: -0.196499
Standard error............: 0.376612
Odds ratio (effect size)..: 0.822
Lower 95% CI..............: 0.393
Upper 95% CI..............: 1.719
T-value...................: -0.521755
P-value...................: 0.6018406
Sample size in model......: 540
Number of events..........: 43
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 43
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 6.451e-03 1.006e+00 3.229e-01 0.020 0.98406
Age -2.572e-04 9.997e-01 2.281e-02 -0.011 0.99100
Gendermale 1.305e+00 3.688e+00 5.417e-01 2.409 0.01599 *
ORdate_year2002 1.742e+01 3.678e+07 3.449e+04 0.001 0.99960
ORdate_year2003 1.702e+01 2.467e+07 3.449e+04 0.000 0.99961
ORdate_year2004 1.614e+01 1.026e+07 3.449e+04 0.000 0.99963
ORdate_year2005 1.714e+01 2.783e+07 3.449e+04 0.000 0.99960
ORdate_year2006 1.693e+01 2.249e+07 3.449e+04 0.000 0.99961
ORdate_year2007 1.675e+01 1.882e+07 3.449e+04 0.000 0.99961
ORdate_year2008 1.688e+01 2.143e+07 3.449e+04 0.000 0.99961
ORdate_year2009 1.647e+01 1.419e+07 3.449e+04 0.000 0.99962
ORdate_year2010 1.551e+01 5.432e+06 3.449e+04 0.000 0.99964
ORdate_year2011 1.575e+01 6.914e+06 3.449e+04 0.000 0.99964
ORdate_year2012 1.648e+01 1.429e+07 3.449e+04 0.000 0.99962
ORdate_year2013 -1.827e+00 1.609e-01 3.786e+04 0.000 0.99996
ORdate_year2014 -7.799e-01 4.585e-01 4.877e+04 0.000 0.99999
ORdate_year2015 -2.643e-01 7.678e-01 3.765e+04 0.000 0.99999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.699e+00 1.829e-01 1.029e+00 -1.650 0.09884 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.791e-01 1.322e+00 3.482e-01 0.802 0.42278
SmokerStatusEx-smoker -3.059e-01 7.365e-01 3.514e-01 -0.871 0.38397
SmokerStatusNever smoked 1.046e-01 1.110e+00 4.732e-01 0.221 0.82499
Med.Statin.LLDno -3.699e-01 6.908e-01 4.363e-01 -0.848 0.39657
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -4.584e-02 9.552e-01 5.632e-01 -0.081 0.93513
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.484e-02 9.853e-01 8.221e-03 -1.805 0.07108 .
BMI 1.973e-02 1.020e+00 4.223e-02 0.467 0.64043
MedHx_CVDNo -1.200e+00 3.012e-01 4.263e-01 -2.815 0.00488 **
stenose0-49% -1.768e+00 1.706e-01 3.928e+04 0.000 0.99996
stenose50-70% -7.472e-01 4.737e-01 3.491e+04 0.000 0.99998
stenose70-90% 1.716e+01 2.831e+07 3.449e+04 0.000 0.99960
stenose90-99% 1.695e+01 2.306e+07 3.449e+04 0.000 0.99961
stenose100% (Occlusion) -5.348e-01 5.858e-01 3.739e+04 0.000 0.99999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.006e+00 9.936e-01 0.53450 1.8952
Age 9.997e-01 1.000e+00 0.95603 1.0455
Gendermale 3.688e+00 2.712e-01 1.27554 10.6622
ORdate_year2002 3.678e+07 2.719e-08 0.00000 Inf
ORdate_year2003 2.467e+07 4.054e-08 0.00000 Inf
ORdate_year2004 1.026e+07 9.744e-08 0.00000 Inf
ORdate_year2005 2.783e+07 3.594e-08 0.00000 Inf
ORdate_year2006 2.249e+07 4.446e-08 0.00000 Inf
ORdate_year2007 1.882e+07 5.314e-08 0.00000 Inf
ORdate_year2008 2.143e+07 4.667e-08 0.00000 Inf
ORdate_year2009 1.419e+07 7.048e-08 0.00000 Inf
ORdate_year2010 5.432e+06 1.841e-07 0.00000 Inf
ORdate_year2011 6.914e+06 1.446e-07 0.00000 Inf
ORdate_year2012 1.429e+07 6.997e-08 0.00000 Inf
ORdate_year2013 1.609e-01 6.213e+00 0.00000 Inf
ORdate_year2014 4.585e-01 2.181e+00 0.00000 Inf
ORdate_year2015 7.678e-01 1.302e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 1.829e-01 5.467e+00 0.02433 1.3751
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.322e+00 7.565e-01 0.66809 2.6158
SmokerStatusEx-smoker 7.365e-01 1.358e+00 0.36987 1.4663
SmokerStatusNever smoked 1.110e+00 9.007e-01 0.43922 2.8067
Med.Statin.LLDno 6.908e-01 1.448e+00 0.29375 1.6246
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.552e-01 1.047e+00 0.31674 2.8806
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.853e-01 1.015e+00 0.96952 1.0013
BMI 1.020e+00 9.805e-01 0.93890 1.1079
MedHx_CVDNo 3.012e-01 3.320e+00 0.13060 0.6945
stenose0-49% 1.706e-01 5.860e+00 0.00000 Inf
stenose50-70% 4.737e-01 2.111e+00 0.00000 Inf
stenose70-90% 2.831e+07 3.533e-08 0.00000 Inf
stenose90-99% 2.306e+07 4.336e-08 0.00000 Inf
stenose100% (Occlusion) 5.858e-01 1.707e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.779 (se = 0.028 )
Likelihood ratio test= 45.66 on 31 df, p=0.04
Wald test = 23.93 on 31 df, p=0.8
Score (logrank) test = 37.35 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: COL4A1
Effect size...............: 0.006451
Standard error............: 0.322905
Odds ratio (effect size)..: 1.006
Lower 95% CI..............: 0.534
Upper 95% CI..............: 1.895
T-value...................: 0.019978
P-value...................: 0.9840608
Sample size in model......: 540
Number of events..........: 43
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 43
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 3.084e-01 1.361e+00 3.214e-01 0.960 0.33726
Age -9.853e-04 9.990e-01 2.294e-02 -0.043 0.96575
Gendermale 1.355e+00 3.878e+00 5.474e-01 2.476 0.01327 *
ORdate_year2002 1.660e+01 1.614e+07 2.098e+04 0.001 0.99937
ORdate_year2003 1.618e+01 1.062e+07 2.098e+04 0.001 0.99938
ORdate_year2004 1.521e+01 4.049e+06 2.098e+04 0.001 0.99942
ORdate_year2005 1.625e+01 1.139e+07 2.098e+04 0.001 0.99938
ORdate_year2006 1.603e+01 9.193e+06 2.098e+04 0.001 0.99939
ORdate_year2007 1.594e+01 8.353e+06 2.098e+04 0.001 0.99939
ORdate_year2008 1.604e+01 9.241e+06 2.098e+04 0.001 0.99939
ORdate_year2009 1.552e+01 5.496e+06 2.098e+04 0.001 0.99941
ORdate_year2010 1.461e+01 2.222e+06 2.098e+04 0.001 0.99944
ORdate_year2011 1.488e+01 2.899e+06 2.098e+04 0.001 0.99943
ORdate_year2012 1.560e+01 5.948e+06 2.098e+04 0.001 0.99941
ORdate_year2013 -1.661e+00 1.899e-01 2.317e+04 0.000 0.99994
ORdate_year2014 -7.943e-01 4.519e-01 2.967e+04 0.000 0.99998
ORdate_year2015 -1.756e-01 8.389e-01 2.275e+04 0.000 0.99999
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.713e+00 1.803e-01 1.030e+00 -1.663 0.09628 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.805e-01 1.324e+00 3.481e-01 0.806 0.42038
SmokerStatusEx-smoker -3.023e-01 7.391e-01 3.523e-01 -0.858 0.39085
SmokerStatusNever smoked 1.266e-01 1.135e+00 4.740e-01 0.267 0.78935
Med.Statin.LLDno -3.666e-01 6.931e-01 4.365e-01 -0.840 0.40093
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -2.795e-02 9.724e-01 5.624e-01 -0.050 0.96037
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.561e-02 9.845e-01 8.234e-03 -1.896 0.05802 .
BMI 2.322e-02 1.023e+00 4.225e-02 0.550 0.58262
MedHx_CVDNo -1.209e+00 2.985e-01 4.275e-01 -2.829 0.00468 **
stenose0-49% -1.950e+00 1.423e-01 2.421e+04 0.000 0.99994
stenose50-70% -9.331e-01 3.933e-01 2.123e+04 0.000 0.99996
stenose70-90% 1.597e+01 8.663e+06 2.098e+04 0.001 0.99939
stenose90-99% 1.576e+01 6.991e+06 2.098e+04 0.001 0.99940
stenose100% (Occlusion) -6.929e-01 5.001e-01 2.292e+04 0.000 0.99998
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.361e+00 7.346e-01 0.72508 2.5554
Age 9.990e-01 1.001e+00 0.95509 1.0450
Gendermale 3.878e+00 2.578e-01 1.32661 11.3392
ORdate_year2002 1.614e+07 6.196e-08 0.00000 Inf
ORdate_year2003 1.062e+07 9.420e-08 0.00000 Inf
ORdate_year2004 4.049e+06 2.470e-07 0.00000 Inf
ORdate_year2005 1.139e+07 8.776e-08 0.00000 Inf
ORdate_year2006 9.193e+06 1.088e-07 0.00000 Inf
ORdate_year2007 8.353e+06 1.197e-07 0.00000 Inf
ORdate_year2008 9.241e+06 1.082e-07 0.00000 Inf
ORdate_year2009 5.496e+06 1.819e-07 0.00000 Inf
ORdate_year2010 2.222e+06 4.501e-07 0.00000 Inf
ORdate_year2011 2.899e+06 3.450e-07 0.00000 Inf
ORdate_year2012 5.948e+06 1.681e-07 0.00000 Inf
ORdate_year2013 1.899e-01 5.265e+00 0.00000 Inf
ORdate_year2014 4.519e-01 2.213e+00 0.00000 Inf
ORdate_year2015 8.389e-01 1.192e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 1.803e-01 5.545e+00 0.02395 1.3575
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.324e+00 7.554e-01 0.66910 2.6192
SmokerStatusEx-smoker 7.391e-01 1.353e+00 0.37051 1.4744
SmokerStatusNever smoked 1.135e+00 8.811e-01 0.44826 2.8738
Med.Statin.LLDno 6.931e-01 1.443e+00 0.29461 1.6304
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.724e-01 1.028e+00 0.32295 2.9282
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.845e-01 1.016e+00 0.96875 1.0005
BMI 1.023e+00 9.770e-01 0.94215 1.1118
MedHx_CVDNo 2.985e-01 3.350e+00 0.12914 0.6899
stenose0-49% 1.423e-01 7.025e+00 0.00000 Inf
stenose50-70% 3.933e-01 2.542e+00 0.00000 Inf
stenose70-90% 8.663e+06 1.154e-07 0.00000 Inf
stenose90-99% 6.991e+06 1.430e-07 0.00000 Inf
stenose100% (Occlusion) 5.001e-01 2.000e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.782 (se = 0.028 )
Likelihood ratio test= 46.58 on 31 df, p=0.04
Wald test = 24.55 on 31 df, p=0.8
Score (logrank) test = 37.97 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: COL4A2
Effect size...............: 0.308371
Standard error............: 0.321353
Odds ratio (effect size)..: 1.361
Lower 95% CI..............: 0.725
Upper 95% CI..............: 2.555
T-value...................: 0.9596
P-value...................: 0.3372565
Sample size in model......: 540
Number of events..........: 43
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 43
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -2.853e-01 7.518e-01 3.219e-01 -0.886 0.3754
Age 8.306e-04 1.001e+00 2.284e-02 0.036 0.9710
Gendermale 1.295e+00 3.650e+00 5.385e-01 2.404 0.0162 *
ORdate_year2002 1.756e+01 4.239e+07 3.462e+04 0.001 0.9996
ORdate_year2003 1.719e+01 2.908e+07 3.462e+04 0.000 0.9996
ORdate_year2004 1.629e+01 1.188e+07 3.462e+04 0.000 0.9996
ORdate_year2005 1.733e+01 3.347e+07 3.462e+04 0.001 0.9996
ORdate_year2006 1.712e+01 2.716e+07 3.462e+04 0.000 0.9996
ORdate_year2007 1.692e+01 2.233e+07 3.462e+04 0.000 0.9996
ORdate_year2008 1.701e+01 2.442e+07 3.462e+04 0.000 0.9996
ORdate_year2009 1.671e+01 1.804e+07 3.462e+04 0.000 0.9996
ORdate_year2010 1.570e+01 6.585e+06 3.462e+04 0.000 0.9996
ORdate_year2011 1.589e+01 7.986e+06 3.462e+04 0.000 0.9996
ORdate_year2012 1.660e+01 1.620e+07 3.462e+04 0.000 0.9996
ORdate_year2013 -1.617e+00 1.985e-01 3.797e+04 0.000 1.0000
ORdate_year2014 -4.542e-01 6.349e-01 4.896e+04 0.000 1.0000
ORdate_year2015 -7.378e-03 9.926e-01 3.798e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.718e+00 1.793e-01 1.030e+00 -1.669 0.0952 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.943e-01 1.342e+00 3.499e-01 0.841 0.4003
SmokerStatusEx-smoker -2.959e-01 7.439e-01 3.516e-01 -0.842 0.4000
SmokerStatusNever smoked 1.058e-01 1.112e+00 4.736e-01 0.223 0.8232
Med.Statin.LLDno -3.856e-01 6.800e-01 4.367e-01 -0.883 0.3773
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -1.157e-01 8.908e-01 5.688e-01 -0.203 0.8388
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.416e-02 9.859e-01 8.202e-03 -1.726 0.0843 .
BMI 1.963e-02 1.020e+00 4.203e-02 0.467 0.6403
MedHx_CVDNo -1.175e+00 3.088e-01 4.259e-01 -2.759 0.0058 **
stenose0-49% -1.531e+00 2.164e-01 3.953e+04 0.000 1.0000
stenose50-70% -6.089e-01 5.440e-01 3.505e+04 0.000 1.0000
stenose70-90% 1.726e+01 3.145e+07 3.462e+04 0.000 0.9996
stenose90-99% 1.706e+01 2.572e+07 3.462e+04 0.000 0.9996
stenose100% (Occlusion) -4.340e-01 6.479e-01 3.728e+04 0.000 1.0000
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 7.518e-01 1.330e+00 0.40004 1.4128
Age 1.001e+00 9.992e-01 0.95702 1.0466
Gendermale 3.650e+00 2.740e-01 1.27032 10.4861
ORdate_year2002 4.239e+07 2.359e-08 0.00000 Inf
ORdate_year2003 2.908e+07 3.439e-08 0.00000 Inf
ORdate_year2004 1.188e+07 8.417e-08 0.00000 Inf
ORdate_year2005 3.347e+07 2.988e-08 0.00000 Inf
ORdate_year2006 2.716e+07 3.681e-08 0.00000 Inf
ORdate_year2007 2.233e+07 4.477e-08 0.00000 Inf
ORdate_year2008 2.442e+07 4.095e-08 0.00000 Inf
ORdate_year2009 1.804e+07 5.544e-08 0.00000 Inf
ORdate_year2010 6.585e+06 1.518e-07 0.00000 Inf
ORdate_year2011 7.986e+06 1.252e-07 0.00000 Inf
ORdate_year2012 1.620e+07 6.171e-08 0.00000 Inf
ORdate_year2013 1.985e-01 5.038e+00 0.00000 Inf
ORdate_year2014 6.349e-01 1.575e+00 0.00000 Inf
ORdate_year2015 9.926e-01 1.007e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 1.793e-01 5.576e+00 0.02382 1.3501
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.342e+00 7.450e-01 0.67602 2.6649
SmokerStatusEx-smoker 7.439e-01 1.344e+00 0.37345 1.4817
SmokerStatusNever smoked 1.112e+00 8.996e-01 0.43941 2.8123
Med.Statin.LLDno 6.800e-01 1.470e+00 0.28895 1.6005
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 8.908e-01 1.123e+00 0.29213 2.7161
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.859e-01 1.014e+00 0.97022 1.0019
BMI 1.020e+00 9.806e-01 0.93919 1.1074
MedHx_CVDNo 3.088e-01 3.239e+00 0.13399 0.7115
stenose0-49% 2.164e-01 4.622e+00 0.00000 Inf
stenose50-70% 5.440e-01 1.838e+00 0.00000 Inf
stenose70-90% 3.145e+07 3.180e-08 0.00000 Inf
stenose90-99% 2.572e+07 3.888e-08 0.00000 Inf
stenose100% (Occlusion) 6.479e-01 1.543e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.781 (se = 0.028 )
Likelihood ratio test= 46.45 on 31 df, p=0.04
Wald test = 24.71 on 31 df, p=0.8
Score (logrank) test = 38.21 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: LDLR
Effect size...............: -0.2853
Standard error............: 0.321883
Odds ratio (effect size)..: 0.752
Lower 95% CI..............: 0.4
Upper 95% CI..............: 1.413
T-value...................: -0.886347
P-value...................: 0.3754308
Sample size in model......: 540
Number of events..........: 43
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 43
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.242e-01 1.132e+00 3.271e-01 0.380 0.70411
Age -3.242e-04 9.997e-01 2.275e-02 -0.014 0.98863
Gendermale 1.317e+00 3.733e+00 5.425e-01 2.428 0.01517 *
ORdate_year2002 1.751e+01 4.012e+07 3.447e+04 0.001 0.99959
ORdate_year2003 1.710e+01 2.665e+07 3.447e+04 0.000 0.99960
ORdate_year2004 1.619e+01 1.079e+07 3.447e+04 0.000 0.99963
ORdate_year2005 1.719e+01 2.929e+07 3.447e+04 0.000 0.99960
ORdate_year2006 1.699e+01 2.388e+07 3.447e+04 0.000 0.99961
ORdate_year2007 1.681e+01 1.995e+07 3.447e+04 0.000 0.99961
ORdate_year2008 1.695e+01 2.296e+07 3.447e+04 0.000 0.99961
ORdate_year2009 1.650e+01 1.466e+07 3.447e+04 0.000 0.99962
ORdate_year2010 1.554e+01 5.634e+06 3.447e+04 0.000 0.99964
ORdate_year2011 1.581e+01 7.338e+06 3.447e+04 0.000 0.99963
ORdate_year2012 1.652e+01 1.488e+07 3.447e+04 0.000 0.99962
ORdate_year2013 -1.713e+00 1.803e-01 3.785e+04 0.000 0.99996
ORdate_year2014 -7.723e-01 4.619e-01 4.875e+04 0.000 0.99999
ORdate_year2015 -2.306e-01 7.940e-01 3.756e+04 0.000 1.00000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.703e+00 1.822e-01 1.029e+00 -1.654 0.09803 .
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 2.882e-01 1.334e+00 3.482e-01 0.828 0.40781
SmokerStatusEx-smoker -3.080e-01 7.349e-01 3.512e-01 -0.877 0.38039
SmokerStatusNever smoked 1.297e-01 1.138e+00 4.778e-01 0.271 0.78603
Med.Statin.LLDno -3.680e-01 6.921e-01 4.368e-01 -0.843 0.39946
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno -2.490e-02 9.754e-01 5.644e-01 -0.044 0.96481
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -1.517e-02 9.849e-01 8.249e-03 -1.839 0.06588 .
BMI 1.966e-02 1.020e+00 4.229e-02 0.465 0.64197
MedHx_CVDNo -1.209e+00 2.984e-01 4.268e-01 -2.833 0.00461 **
stenose0-49% -1.837e+00 1.592e-01 3.944e+04 0.000 0.99996
stenose50-70% -8.221e-01 4.395e-01 3.489e+04 0.000 0.99998
stenose70-90% 1.707e+01 2.587e+07 3.447e+04 0.000 0.99960
stenose90-99% 1.687e+01 2.120e+07 3.447e+04 0.000 0.99961
stenose100% (Occlusion) -6.098e-01 5.434e-01 3.747e+04 0.000 0.99999
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.132e+00 8.832e-01 0.59636 2.1498
Age 9.997e-01 1.000e+00 0.95608 1.0453
Gendermale 3.733e+00 2.679e-01 1.28919 10.8099
ORdate_year2002 4.012e+07 2.492e-08 0.00000 Inf
ORdate_year2003 2.665e+07 3.752e-08 0.00000 Inf
ORdate_year2004 1.079e+07 9.264e-08 0.00000 Inf
ORdate_year2005 2.929e+07 3.414e-08 0.00000 Inf
ORdate_year2006 2.388e+07 4.187e-08 0.00000 Inf
ORdate_year2007 1.995e+07 5.012e-08 0.00000 Inf
ORdate_year2008 2.296e+07 4.356e-08 0.00000 Inf
ORdate_year2009 1.466e+07 6.821e-08 0.00000 Inf
ORdate_year2010 5.634e+06 1.775e-07 0.00000 Inf
ORdate_year2011 7.338e+06 1.363e-07 0.00000 Inf
ORdate_year2012 1.488e+07 6.720e-08 0.00000 Inf
ORdate_year2013 1.803e-01 5.546e+00 0.00000 Inf
ORdate_year2014 4.619e-01 2.165e+00 0.00000 Inf
ORdate_year2015 7.940e-01 1.259e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 1.822e-01 5.489e+00 0.02424 1.3694
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 1.334e+00 7.496e-01 0.67421 2.6396
SmokerStatusEx-smoker 7.349e-01 1.361e+00 0.36924 1.4626
SmokerStatusNever smoked 1.138e+00 8.784e-01 0.44634 2.9039
Med.Statin.LLDno 6.921e-01 1.445e+00 0.29400 1.6292
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 9.754e-01 1.025e+00 0.32267 2.9485
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.849e-01 1.015e+00 0.96915 1.0010
BMI 1.020e+00 9.805e-01 0.93873 1.1080
MedHx_CVDNo 2.984e-01 3.351e+00 0.12928 0.6889
stenose0-49% 1.592e-01 6.280e+00 0.00000 Inf
stenose50-70% 4.395e-01 2.275e+00 0.00000 Inf
stenose70-90% 2.587e+07 3.866e-08 0.00000 Inf
stenose90-99% 2.120e+07 4.717e-08 0.00000 Inf
stenose100% (Occlusion) 5.434e-01 1.840e+00 0.00000 Inf
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.779 (se = 0.028 )
Likelihood ratio test= 45.8 on 31 df, p=0.04
Wald test = 23.86 on 31 df, p=0.8
Score (logrank) test = 37.35 on 31 df, p=0.2
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epcoronary.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcoronary.3years
Protein...................: CD36
Effect size...............: 0.124232
Standard error............: 0.32712
Odds ratio (effect size)..: 1.132
Lower 95% CI..............: 0.596
Upper 95% CI..............: 2.15
T-value...................: 0.379774
P-value...................: 0.7041129
Sample size in model......: 540
Number of events..........: 43
* Analyzing the effect of plaque target-of-interest on [epcvdeath.3years].
- creating temporary SE for this work.
- making a 'Surv' object and adding this to temporary dataframe.
- making strata of each of the plaque target-of-interest and start survival analysis.
> processing [CXCL10]; 1 out of 6 target-of-interest.
> cross tabulation of CXCL10-stratum.
[0, 2) [2,45]
428 194
> fitting the model for CXCL10-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 24
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 3.835e-01 1.467e+00 4.484e-01 0.855 0.3925
Age 8.470e-02 1.088e+00 3.473e-02 2.439 0.0147 *
Gendermale 4.090e-01 1.505e+00 5.810e-01 0.704 0.4815
ORdate_year2002 1.867e+01 1.284e+08 8.023e+04 0.000 0.9998
ORdate_year2003 1.726e+01 3.126e+07 8.023e+04 0.000 0.9998
ORdate_year2004 1.733e+01 3.367e+07 8.023e+04 0.000 0.9998
ORdate_year2005 1.808e+01 7.095e+07 8.023e+04 0.000 0.9998
ORdate_year2006 1.716e+01 2.822e+07 8.023e+04 0.000 0.9998
ORdate_year2007 1.743e+01 3.713e+07 8.023e+04 0.000 0.9998
ORdate_year2008 1.809e+01 7.170e+07 8.023e+04 0.000 0.9998
ORdate_year2009 1.688e+01 2.140e+07 8.023e+04 0.000 0.9998
ORdate_year2010 1.790e+01 5.948e+07 8.023e+04 0.000 0.9998
ORdate_year2011 -2.248e+00 1.056e-01 8.146e+04 0.000 1.0000
ORdate_year2012 -1.688e+00 1.849e-01 8.244e+04 0.000 1.0000
ORdate_year2013 -1.788e+00 1.672e-01 9.457e+04 0.000 1.0000
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 -1.536e+00 2.152e-01 9.027e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.929e+01 4.183e-09 7.510e+03 -0.003 0.9980
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.065e+00 2.901e+00 4.734e-01 2.250 0.0244 *
SmokerStatusEx-smoker -7.947e-01 4.517e-01 4.798e-01 -1.656 0.0977 .
SmokerStatusNever smoked -1.257e+00 2.845e-01 8.283e-01 -1.518 0.1291
Med.Statin.LLDno 5.590e-01 1.749e+00 4.712e-01 1.186 0.2355
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 9.058e-01 2.474e+00 5.776e-01 1.568 0.1169
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.547e-02 9.749e-01 1.146e-02 -2.222 0.0263 *
BMI 3.371e-02 1.034e+00 5.852e-02 0.576 0.5645
MedHx_CVDNo -6.341e-01 5.304e-01 5.413e-01 -1.172 0.2414
stenose0-49% -3.768e+01 4.318e-17 5.840e+04 -0.001 0.9995
stenose50-70% -1.849e+01 9.342e-09 2.801e+04 -0.001 0.9995
stenose70-90% -1.860e+01 8.354e-09 2.801e+04 -0.001 0.9995
stenose90-99% -1.847e+01 9.563e-09 2.801e+04 -0.001 0.9995
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,45] 1.467e+00 6.815e-01 0.60929 3.534
Age 1.088e+00 9.188e-01 1.01676 1.165
Gendermale 1.505e+00 6.643e-01 0.48204 4.701
ORdate_year2002 1.284e+08 7.786e-09 0.00000 Inf
ORdate_year2003 3.126e+07 3.199e-08 0.00000 Inf
ORdate_year2004 3.367e+07 2.970e-08 0.00000 Inf
ORdate_year2005 7.095e+07 1.409e-08 0.00000 Inf
ORdate_year2006 2.822e+07 3.544e-08 0.00000 Inf
ORdate_year2007 3.713e+07 2.693e-08 0.00000 Inf
ORdate_year2008 7.170e+07 1.395e-08 0.00000 Inf
ORdate_year2009 2.140e+07 4.673e-08 0.00000 Inf
ORdate_year2010 5.948e+07 1.681e-08 0.00000 Inf
ORdate_year2011 1.056e-01 9.470e+00 0.00000 Inf
ORdate_year2012 1.849e-01 5.409e+00 0.00000 Inf
ORdate_year2013 1.672e-01 5.980e+00 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 2.152e-01 4.648e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.183e-09 2.391e+08 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 2.901e+00 3.447e-01 1.14720 7.336
SmokerStatusEx-smoker 4.517e-01 2.214e+00 0.17637 1.157
SmokerStatusNever smoked 2.845e-01 3.515e+00 0.05611 1.442
Med.Statin.LLDno 1.749e+00 5.718e-01 0.69446 4.404
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.474e+00 4.042e-01 0.79743 7.674
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.749e-01 1.026e+00 0.95320 0.997
BMI 1.034e+00 9.669e-01 0.92221 1.160
MedHx_CVDNo 5.304e-01 1.885e+00 0.18360 1.532
stenose0-49% 4.318e-17 2.316e+16 0.00000 Inf
stenose50-70% 9.342e-09 1.070e+08 0.00000 Inf
stenose70-90% 8.354e-09 1.197e+08 0.00000 Inf
stenose90-99% 9.563e-09 1.046e+08 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.857 (se = 0.03 )
Likelihood ratio test= 47.12 on 29 df, p=0.02
Wald test = 22.88 on 29 df, p=0.8
Score (logrank) test = 41.75 on 29 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CXCL10 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: CXCL10
Effect size...............: 0.383475
Standard error............: 0.448449
Odds ratio (effect size)..: 1.467
Lower 95% CI..............: 0.609
Upper 95% CI..............: 3.534
T-value...................: 0.855115
P-value...................: 0.3924878
Sample size in model......: 540
Number of events..........: 24
> processing [PCSK9]; 2 out of 6 target-of-interest.
> cross tabulation of PCSK9-stratum.
[0, 2) [2,13]
438 184
> fitting the model for PCSK9-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 24
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 5.791e-01 1.784e+00 4.756e-01 1.218 0.2234
Age 8.520e-02 1.089e+00 3.485e-02 2.445 0.0145 *
Gendermale 5.326e-01 1.703e+00 5.945e-01 0.896 0.3704
ORdate_year2002 1.868e+01 1.302e+08 7.951e+04 0.000 0.9998
ORdate_year2003 1.706e+01 2.556e+07 7.951e+04 0.000 0.9998
ORdate_year2004 1.736e+01 3.463e+07 7.951e+04 0.000 0.9998
ORdate_year2005 1.812e+01 7.422e+07 7.951e+04 0.000 0.9998
ORdate_year2006 1.700e+01 2.414e+07 7.951e+04 0.000 0.9998
ORdate_year2007 1.731e+01 3.304e+07 7.951e+04 0.000 0.9998
ORdate_year2008 1.791e+01 5.974e+07 7.951e+04 0.000 0.9998
ORdate_year2009 1.677e+01 1.923e+07 7.951e+04 0.000 0.9998
ORdate_year2010 1.783e+01 5.516e+07 7.951e+04 0.000 0.9998
ORdate_year2011 -2.421e+00 8.882e-02 8.058e+04 0.000 1.0000
ORdate_year2012 -1.838e+00 1.591e-01 8.153e+04 0.000 1.0000
ORdate_year2013 -1.978e+00 1.383e-01 9.348e+04 0.000 1.0000
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 -1.729e+00 1.774e-01 8.823e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.931e+01 4.099e-09 7.354e+03 -0.003 0.9979
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.110e+00 3.035e+00 4.715e-01 2.355 0.0185 *
SmokerStatusEx-smoker -8.508e-01 4.271e-01 4.846e-01 -1.756 0.0791 .
SmokerStatusNever smoked -1.151e+00 3.164e-01 8.289e-01 -1.388 0.1650
Med.Statin.LLDno 5.287e-01 1.697e+00 4.733e-01 1.117 0.2640
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.006e+00 2.735e+00 5.734e-01 1.754 0.0794 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.391e-02 9.764e-01 1.118e-02 -2.139 0.0324 *
BMI 3.430e-02 1.035e+00 5.808e-02 0.591 0.5548
MedHx_CVDNo -6.441e-01 5.251e-01 5.358e-01 -1.202 0.2293
stenose0-49% -3.790e+01 3.470e-17 4.862e+04 -0.001 0.9994
stenose50-70% -1.851e+01 9.113e-09 2.694e+04 -0.001 0.9995
stenose70-90% -1.859e+01 8.453e-09 2.694e+04 -0.001 0.9994
stenose90-99% -1.853e+01 8.997e-09 2.694e+04 -0.001 0.9995
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][2,13] 1.784e+00 5.604e-01 0.70250 4.533
Age 1.089e+00 9.183e-01 1.01704 1.166
Gendermale 1.703e+00 5.871e-01 0.53117 5.462
ORdate_year2002 1.302e+08 7.679e-09 0.00000 Inf
ORdate_year2003 2.556e+07 3.912e-08 0.00000 Inf
ORdate_year2004 3.463e+07 2.888e-08 0.00000 Inf
ORdate_year2005 7.422e+07 1.347e-08 0.00000 Inf
ORdate_year2006 2.414e+07 4.142e-08 0.00000 Inf
ORdate_year2007 3.304e+07 3.027e-08 0.00000 Inf
ORdate_year2008 5.974e+07 1.674e-08 0.00000 Inf
ORdate_year2009 1.923e+07 5.199e-08 0.00000 Inf
ORdate_year2010 5.516e+07 1.813e-08 0.00000 Inf
ORdate_year2011 8.882e-02 1.126e+01 0.00000 Inf
ORdate_year2012 1.591e-01 6.287e+00 0.00000 Inf
ORdate_year2013 1.383e-01 7.229e+00 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 1.774e-01 5.637e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.099e-09 2.440e+08 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 3.035e+00 3.295e-01 1.20456 7.646
SmokerStatusEx-smoker 4.271e-01 2.341e+00 0.16521 1.104
SmokerStatusNever smoked 3.164e-01 3.161e+00 0.06232 1.606
Med.Statin.LLDno 1.697e+00 5.894e-01 0.67097 4.291
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.735e+00 3.657e-01 0.88883 8.415
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.764e-01 1.024e+00 0.95522 0.998
BMI 1.035e+00 9.663e-01 0.92355 1.160
MedHx_CVDNo 5.251e-01 1.904e+00 0.18372 1.501
stenose0-49% 3.470e-17 2.882e+16 0.00000 Inf
stenose50-70% 9.113e-09 1.097e+08 0.00000 Inf
stenose70-90% 8.453e-09 1.183e+08 0.00000 Inf
stenose90-99% 8.997e-09 1.111e+08 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.859 (se = 0.03 )
Likelihood ratio test= 47.83 on 29 df, p=0.02
Wald test = 23.52 on 29 df, p=0.8
Score (logrank) test = 42.18 on 29 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' PCSK9 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: PCSK9
Effect size...............: 0.579098
Standard error............: 0.475625
Odds ratio (effect size)..: 1.784
Lower 95% CI..............: 0.702
Upper 95% CI..............: 4.533
T-value...................: 1.217551
P-value...................: 0.2233946
Sample size in model......: 540
Number of events..........: 24
> processing [COL4A1]; 3 out of 6 target-of-interest.
> cross tabulation of COL4A1-stratum.
[ 16, 137) [137,2339]
314 308
> fitting the model for COL4A1-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 24
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 3.733e-01 1.453e+00 4.516e-01 0.827 0.4085
Age 8.220e-02 1.086e+00 3.374e-02 2.436 0.0148 *
Gendermale 4.491e-01 1.567e+00 5.899e-01 0.761 0.4465
ORdate_year2002 1.894e+01 1.689e+08 7.812e+04 0.000 0.9998
ORdate_year2003 1.743e+01 3.720e+07 7.812e+04 0.000 0.9998
ORdate_year2004 1.753e+01 4.106e+07 7.812e+04 0.000 0.9998
ORdate_year2005 1.830e+01 8.833e+07 7.812e+04 0.000 0.9998
ORdate_year2006 1.728e+01 3.198e+07 7.812e+04 0.000 0.9998
ORdate_year2007 1.766e+01 4.675e+07 7.812e+04 0.000 0.9998
ORdate_year2008 1.827e+01 8.599e+07 7.812e+04 0.000 0.9998
ORdate_year2009 1.700e+01 2.407e+07 7.812e+04 0.000 0.9998
ORdate_year2010 1.808e+01 7.106e+07 7.812e+04 0.000 0.9998
ORdate_year2011 -1.961e+00 1.407e-01 7.929e+04 0.000 1.0000
ORdate_year2012 -1.435e+00 2.382e-01 8.026e+04 0.000 1.0000
ORdate_year2013 -1.685e+00 1.854e-01 9.163e+04 0.000 1.0000
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 -1.542e+00 2.139e-01 8.727e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.924e+01 4.391e-09 7.413e+03 -0.003 0.9979
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.112e+00 3.040e+00 4.710e-01 2.361 0.0182 *
SmokerStatusEx-smoker -7.434e-01 4.755e-01 4.756e-01 -1.563 0.1181
SmokerStatusNever smoked -1.233e+00 2.913e-01 8.302e-01 -1.485 0.1374
Med.Statin.LLDno 5.696e-01 1.768e+00 4.699e-01 1.212 0.2255
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 9.950e-01 2.705e+00 5.786e-01 1.720 0.0855 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.502e-02 9.753e-01 1.137e-02 -2.201 0.0278 *
BMI 3.745e-02 1.038e+00 5.845e-02 0.641 0.5217
MedHx_CVDNo -6.955e-01 4.988e-01 5.401e-01 -1.288 0.1978
stenose0-49% -3.746e+01 5.394e-17 5.499e+04 -0.001 0.9995
stenose50-70% -1.850e+01 9.245e-09 2.747e+04 -0.001 0.9995
stenose70-90% -1.856e+01 8.674e-09 2.747e+04 -0.001 0.9995
stenose90-99% -1.842e+01 9.992e-09 2.747e+04 -0.001 0.9995
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][137,2339] 1.453e+00 6.885e-01 0.59936 3.5201
Age 1.086e+00 9.211e-01 1.01619 1.1599
Gendermale 1.567e+00 6.382e-01 0.49308 4.9795
ORdate_year2002 1.689e+08 5.922e-09 0.00000 Inf
ORdate_year2003 3.720e+07 2.688e-08 0.00000 Inf
ORdate_year2004 4.106e+07 2.436e-08 0.00000 Inf
ORdate_year2005 8.833e+07 1.132e-08 0.00000 Inf
ORdate_year2006 3.198e+07 3.127e-08 0.00000 Inf
ORdate_year2007 4.675e+07 2.139e-08 0.00000 Inf
ORdate_year2008 8.599e+07 1.163e-08 0.00000 Inf
ORdate_year2009 2.407e+07 4.154e-08 0.00000 Inf
ORdate_year2010 7.106e+07 1.407e-08 0.00000 Inf
ORdate_year2011 1.407e-01 7.109e+00 0.00000 Inf
ORdate_year2012 2.382e-01 4.198e+00 0.00000 Inf
ORdate_year2013 1.854e-01 5.394e+00 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 2.139e-01 4.674e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.391e-09 2.277e+08 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 3.040e+00 3.289e-01 1.20793 7.6526
SmokerStatusEx-smoker 4.755e-01 2.103e+00 0.18719 1.2078
SmokerStatusNever smoked 2.913e-01 3.432e+00 0.05724 1.4829
Med.Statin.LLDno 1.768e+00 5.658e-01 0.70368 4.4397
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.705e+00 3.697e-01 0.87021 8.4070
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.753e-01 1.025e+00 0.95379 0.9973
BMI 1.038e+00 9.632e-01 0.92579 1.1642
MedHx_CVDNo 4.988e-01 2.005e+00 0.17308 1.4377
stenose0-49% 5.394e-17 1.854e+16 0.00000 Inf
stenose50-70% 9.245e-09 1.082e+08 0.00000 Inf
stenose70-90% 8.674e-09 1.153e+08 0.00000 Inf
stenose90-99% 9.992e-09 1.001e+08 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.853 (se = 0.032 )
Likelihood ratio test= 47.1 on 29 df, p=0.02
Wald test = 22.27 on 29 df, p=0.8
Score (logrank) test = 41.15 on 29 df, p=0.07
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A1 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: COL4A1
Effect size...............: 0.373295
Standard error............: 0.451632
Odds ratio (effect size)..: 1.453
Lower 95% CI..............: 0.599
Upper 95% CI..............: 3.52
T-value...................: 0.826546
P-value...................: 0.4084942
Sample size in model......: 540
Number of events..........: 24
> processing [COL4A2]; 4 out of 6 target-of-interest.
> cross tabulation of COL4A2-stratum.
[ 7, 154) [154,8415]
312 310
> fitting the model for COL4A2-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 24
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 6.385e-01 1.894e+00 4.401e-01 1.451 0.1468
Age 8.105e-02 1.084e+00 3.366e-02 2.408 0.0160 *
Gendermale 4.758e-01 1.609e+00 5.895e-01 0.807 0.4196
ORdate_year2002 1.906e+01 1.888e+08 7.986e+04 0.000 0.9998
ORdate_year2003 1.760e+01 4.414e+07 7.986e+04 0.000 0.9998
ORdate_year2004 1.763e+01 4.528e+07 7.986e+04 0.000 0.9998
ORdate_year2005 1.838e+01 9.614e+07 7.986e+04 0.000 0.9998
ORdate_year2006 1.737e+01 3.496e+07 7.986e+04 0.000 0.9998
ORdate_year2007 1.784e+01 5.611e+07 7.986e+04 0.000 0.9998
ORdate_year2008 1.847e+01 1.051e+08 7.986e+04 0.000 0.9998
ORdate_year2009 1.707e+01 2.578e+07 7.986e+04 0.000 0.9998
ORdate_year2010 1.814e+01 7.521e+07 7.986e+04 0.000 0.9998
ORdate_year2011 -1.795e+00 1.661e-01 8.104e+04 0.000 1.0000
ORdate_year2012 -1.290e+00 2.752e-01 8.190e+04 0.000 1.0000
ORdate_year2013 -1.417e+00 2.424e-01 9.533e+04 0.000 1.0000
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 -1.490e+00 2.254e-01 8.806e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.917e+01 4.733e-09 7.639e+03 -0.003 0.9980
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.159e+00 3.186e+00 4.793e-01 2.417 0.0156 *
SmokerStatusEx-smoker -7.204e-01 4.866e-01 4.724e-01 -1.525 0.1273
SmokerStatusNever smoked -1.196e+00 3.024e-01 8.324e-01 -1.437 0.1507
Med.Statin.LLDno 6.087e-01 1.838e+00 4.693e-01 1.297 0.1946
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 9.253e-01 2.523e+00 5.744e-01 1.611 0.1072
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.574e-02 9.746e-01 1.128e-02 -2.282 0.0225 *
BMI 3.959e-02 1.040e+00 5.735e-02 0.690 0.4900
MedHx_CVDNo -6.967e-01 4.982e-01 5.442e-01 -1.280 0.2005
stenose0-49% -3.748e+01 5.264e-17 6.101e+04 -0.001 0.9995
stenose50-70% -1.850e+01 9.199e-09 3.078e+04 -0.001 0.9995
stenose70-90% -1.855e+01 8.811e-09 3.078e+04 -0.001 0.9995
stenose90-99% -1.846e+01 9.610e-09 3.078e+04 -0.001 0.9995
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][154,8415] 1.894e+00 5.281e-01 0.79925 4.4863
Age 1.084e+00 9.221e-01 1.01520 1.1584
Gendermale 1.609e+00 6.214e-01 0.50682 5.1096
ORdate_year2002 1.888e+08 5.298e-09 0.00000 Inf
ORdate_year2003 4.414e+07 2.266e-08 0.00000 Inf
ORdate_year2004 4.528e+07 2.209e-08 0.00000 Inf
ORdate_year2005 9.614e+07 1.040e-08 0.00000 Inf
ORdate_year2006 3.496e+07 2.861e-08 0.00000 Inf
ORdate_year2007 5.611e+07 1.782e-08 0.00000 Inf
ORdate_year2008 1.051e+08 9.516e-09 0.00000 Inf
ORdate_year2009 2.578e+07 3.879e-08 0.00000 Inf
ORdate_year2010 7.521e+07 1.330e-08 0.00000 Inf
ORdate_year2011 1.661e-01 6.021e+00 0.00000 Inf
ORdate_year2012 2.752e-01 3.633e+00 0.00000 Inf
ORdate_year2013 2.424e-01 4.126e+00 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 2.254e-01 4.437e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.733e-09 2.113e+08 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 3.186e+00 3.139e-01 1.24515 8.1508
SmokerStatusEx-smoker 4.866e-01 2.055e+00 0.19276 1.2281
SmokerStatusNever smoked 3.024e-01 3.307e+00 0.05916 1.5455
Med.Statin.LLDno 1.838e+00 5.440e-01 0.73259 4.6118
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.523e+00 3.964e-01 0.81837 7.7759
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.746e-01 1.026e+00 0.95327 0.9964
BMI 1.040e+00 9.612e-01 0.92977 1.1641
MedHx_CVDNo 4.982e-01 2.007e+00 0.17147 1.4477
stenose0-49% 5.264e-17 1.900e+16 0.00000 Inf
stenose50-70% 9.199e-09 1.087e+08 0.00000 Inf
stenose70-90% 8.811e-09 1.135e+08 0.00000 Inf
stenose90-99% 9.610e-09 1.041e+08 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.857 (se = 0.03 )
Likelihood ratio test= 48.55 on 29 df, p=0.01
Wald test = 23.84 on 29 df, p=0.7
Score (logrank) test = 42.87 on 29 df, p=0.05
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' COL4A2 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: COL4A2
Effect size...............: 0.638479
Standard error............: 0.440087
Odds ratio (effect size)..: 1.894
Lower 95% CI..............: 0.799
Upper 95% CI..............: 4.486
T-value...................: 1.450799
P-value...................: 0.1468358
Sample size in model......: 540
Number of events..........: 24
> processing [LDLR]; 5 out of 6 target-of-interest.
> cross tabulation of LDLR-stratum.
[ 17, 188) [188,4409]
311 311
> fitting the model for LDLR-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 24
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] -2.804e-01 7.555e-01 4.469e-01 -0.627 0.5305
Age 8.330e-02 1.087e+00 3.470e-02 2.401 0.0164 *
Gendermale 4.362e-01 1.547e+00 5.805e-01 0.751 0.4524
ORdate_year2002 1.879e+01 1.442e+08 7.936e+04 0.000 0.9998
ORdate_year2003 1.741e+01 3.632e+07 7.936e+04 0.000 0.9998
ORdate_year2004 1.756e+01 4.218e+07 7.936e+04 0.000 0.9998
ORdate_year2005 1.833e+01 9.170e+07 7.936e+04 0.000 0.9998
ORdate_year2006 1.732e+01 3.322e+07 7.936e+04 0.000 0.9998
ORdate_year2007 1.767e+01 4.708e+07 7.936e+04 0.000 0.9998
ORdate_year2008 1.821e+01 8.113e+07 7.936e+04 0.000 0.9998
ORdate_year2009 1.717e+01 2.858e+07 7.936e+04 0.000 0.9998
ORdate_year2010 1.815e+01 7.636e+07 7.936e+04 0.000 0.9998
ORdate_year2011 -2.040e+00 1.300e-01 8.056e+04 0.000 1.0000
ORdate_year2012 -1.546e+00 2.131e-01 8.164e+04 0.000 1.0000
ORdate_year2013 -1.591e+00 2.037e-01 9.302e+04 0.000 1.0000
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 -1.443e+00 2.363e-01 8.994e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.926e+01 4.331e-09 7.421e+03 -0.003 0.9979
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.100e+00 3.005e+00 4.721e-01 2.330 0.0198 *
SmokerStatusEx-smoker -8.080e-01 4.457e-01 4.842e-01 -1.669 0.0952 .
SmokerStatusNever smoked -1.265e+00 2.822e-01 8.292e-01 -1.526 0.1270
Med.Statin.LLDno 5.399e-01 1.716e+00 4.689e-01 1.151 0.2496
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 8.622e-01 2.368e+00 5.876e-01 1.467 0.1422
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.509e-02 9.752e-01 1.131e-02 -2.220 0.0265 *
BMI 2.848e-02 1.029e+00 5.983e-02 0.476 0.6341
MedHx_CVDNo -6.343e-01 5.303e-01 5.423e-01 -1.170 0.2421
stenose0-49% -3.723e+01 6.794e-17 5.302e+04 -0.001 0.9994
stenose50-70% -1.812e+01 1.344e-08 2.567e+04 -0.001 0.9994
stenose70-90% -1.830e+01 1.129e-08 2.567e+04 -0.001 0.9994
stenose90-99% -1.818e+01 1.274e-08 2.567e+04 -0.001 0.9994
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][188,4409] 7.555e-01 1.324e+00 0.31464 1.8141
Age 1.087e+00 9.201e-01 1.01541 1.1633
Gendermale 1.547e+00 6.465e-01 0.49579 4.8254
ORdate_year2002 1.442e+08 6.932e-09 0.00000 Inf
ORdate_year2003 3.632e+07 2.753e-08 0.00000 Inf
ORdate_year2004 4.218e+07 2.371e-08 0.00000 Inf
ORdate_year2005 9.170e+07 1.091e-08 0.00000 Inf
ORdate_year2006 3.322e+07 3.011e-08 0.00000 Inf
ORdate_year2007 4.708e+07 2.124e-08 0.00000 Inf
ORdate_year2008 8.113e+07 1.233e-08 0.00000 Inf
ORdate_year2009 2.858e+07 3.499e-08 0.00000 Inf
ORdate_year2010 7.636e+07 1.310e-08 0.00000 Inf
ORdate_year2011 1.300e-01 7.692e+00 0.00000 Inf
ORdate_year2012 2.131e-01 4.692e+00 0.00000 Inf
ORdate_year2013 2.037e-01 4.910e+00 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 2.363e-01 4.233e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.331e-09 2.309e+08 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 3.005e+00 3.328e-01 1.19100 7.5798
SmokerStatusEx-smoker 4.457e-01 2.243e+00 0.17256 1.1514
SmokerStatusNever smoked 2.822e-01 3.544e+00 0.05555 1.4332
Med.Statin.LLDno 1.716e+00 5.828e-01 0.68440 4.3017
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.368e+00 4.222e-01 0.74873 7.4921
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.752e-01 1.025e+00 0.95385 0.9971
BMI 1.029e+00 9.719e-01 0.91503 1.1569
MedHx_CVDNo 5.303e-01 1.886e+00 0.18321 1.5351
stenose0-49% 6.794e-17 1.472e+16 0.00000 Inf
stenose50-70% 1.344e-08 7.439e+07 0.00000 Inf
stenose70-90% 1.129e-08 8.860e+07 0.00000 Inf
stenose90-99% 1.274e-08 7.851e+07 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.854 (se = 0.032 )
Likelihood ratio test= 46.81 on 29 df, p=0.02
Wald test = 22.17 on 29 df, p=0.8
Score (logrank) test = 41.38 on 29 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' LDLR ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: LDLR
Effect size...............: -0.280361
Standard error............: 0.446934
Odds ratio (effect size)..: 0.756
Lower 95% CI..............: 0.315
Upper 95% CI..............: 1.814
T-value...................: -0.627299
P-value...................: 0.5304633
Sample size in model......: 540
Number of events..........: 24
> processing [CD36]; 6 out of 6 target-of-interest.
> cross tabulation of CD36-stratum.
[ 7, 84) [84,1898]
311 311
> fitting the model for CD36-stratum.
> make a Kaplan-Meier-shizzle...
> perform the Cox-regression fashizzle and plot it...


Call:
coxph(formula = Surv(TEMP.DF[, eptime], event) ~ TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]] +
Age + Gender + ORdate_year + Hypertension.composite + DiabetesStatus +
SmokerStatus + Med.Statin.LLD + Med.all.antiplatelet + GFR_MDRD +
BMI + MedHx_CVD + stenose, data = TEMP.DF)
n= 540, number of events= 24
(82 observations deleted due to missingness)
coef exp(coef) se(coef) z Pr(>|z|)
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 3.881e-01 1.474e+00 4.482e-01 0.866 0.3866
Age 8.094e-02 1.084e+00 3.355e-02 2.413 0.0158 *
Gendermale 4.559e-01 1.578e+00 5.870e-01 0.777 0.4374
ORdate_year2002 1.892e+01 1.639e+08 7.807e+04 0.000 0.9998
ORdate_year2003 1.746e+01 3.835e+07 7.807e+04 0.000 0.9998
ORdate_year2004 1.757e+01 4.278e+07 7.807e+04 0.000 0.9998
ORdate_year2005 1.828e+01 8.690e+07 7.807e+04 0.000 0.9998
ORdate_year2006 1.728e+01 3.209e+07 7.807e+04 0.000 0.9998
ORdate_year2007 1.763e+01 4.519e+07 7.807e+04 0.000 0.9998
ORdate_year2008 1.827e+01 8.605e+07 7.807e+04 0.000 0.9998
ORdate_year2009 1.698e+01 2.372e+07 7.807e+04 0.000 0.9998
ORdate_year2010 1.806e+01 6.992e+07 7.807e+04 0.000 0.9998
ORdate_year2011 -1.974e+00 1.389e-01 7.927e+04 0.000 1.0000
ORdate_year2012 -1.524e+00 2.179e-01 8.010e+04 0.000 1.0000
ORdate_year2013 -1.422e+00 2.412e-01 9.153e+04 0.000 1.0000
ORdate_year2014 NA NA 0.000e+00 NA NA
ORdate_year2015 -1.587e+00 2.045e-01 8.706e+04 0.000 1.0000
ORdate_year2016 NA NA 0.000e+00 NA NA
ORdate_year2017 NA NA 0.000e+00 NA NA
ORdate_year2018 NA NA 0.000e+00 NA NA
ORdate_year2019 NA NA 0.000e+00 NA NA
ORdate_year2020 NA NA 0.000e+00 NA NA
ORdate_year2021 NA NA 0.000e+00 NA NA
ORdate_year2022 NA NA 0.000e+00 NA NA
Hypertension.compositeno -1.921e+01 4.533e-09 7.561e+03 -0.003 0.9980
Hypertension.compositeyes NA NA 0.000e+00 NA NA
DiabetesStatusDiabetes 1.131e+00 3.098e+00 4.711e-01 2.400 0.0164 *
SmokerStatusEx-smoker -7.959e-01 4.512e-01 4.742e-01 -1.678 0.0933 .
SmokerStatusNever smoked -1.242e+00 2.887e-01 8.311e-01 -1.495 0.1350
Med.Statin.LLDno 5.451e-01 1.725e+00 4.688e-01 1.163 0.2449
Med.Statin.LLDyes NA NA 0.000e+00 NA NA
Med.all.antiplateletno 1.018e+00 2.769e+00 5.828e-01 1.747 0.0806 .
Med.all.antiplateletyes NA NA 0.000e+00 NA NA
GFR_MDRD -2.544e-02 9.749e-01 1.154e-02 -2.205 0.0275 *
BMI 3.239e-02 1.033e+00 5.885e-02 0.550 0.5821
MedHx_CVDNo -6.923e-01 5.004e-01 5.404e-01 -1.281 0.2001
stenose0-49% -3.752e+01 5.082e-17 5.563e+04 -0.001 0.9995
stenose50-70% -1.849e+01 9.330e-09 2.722e+04 -0.001 0.9995
stenose70-90% -1.857e+01 8.585e-09 2.722e+04 -0.001 0.9995
stenose90-99% -1.844e+01 9.764e-09 2.722e+04 -0.001 0.9995
stenose100% (Occlusion) NA NA 0.000e+00 NA NA
stenoseNA NA NA 0.000e+00 NA NA
stenose50-99% NA NA 0.000e+00 NA NA
stenose70-99% NA NA 0.000e+00 NA NA
stenose99 NA NA 0.000e+00 NA NA
---
Signif. codes: 0 '***' 0.001 '**' 0.01 '*' 0.05 '.' 0.1 ' ' 1
exp(coef) exp(-coef) lower .95 upper .95
TEMP.DF[[TRAITS.TARGET.RANK[target_of_interest]]][84,1898] 1.474e+00 6.784e-01 0.61236 3.5486
Age 1.084e+00 9.222e-01 1.01530 1.1580
Gendermale 1.578e+00 6.339e-01 0.49923 4.9849
ORdate_year2002 1.639e+08 6.099e-09 0.00000 Inf
ORdate_year2003 3.835e+07 2.608e-08 0.00000 Inf
ORdate_year2004 4.278e+07 2.337e-08 0.00000 Inf
ORdate_year2005 8.690e+07 1.151e-08 0.00000 Inf
ORdate_year2006 3.209e+07 3.116e-08 0.00000 Inf
ORdate_year2007 4.519e+07 2.213e-08 0.00000 Inf
ORdate_year2008 8.605e+07 1.162e-08 0.00000 Inf
ORdate_year2009 2.372e+07 4.215e-08 0.00000 Inf
ORdate_year2010 6.992e+07 1.430e-08 0.00000 Inf
ORdate_year2011 1.389e-01 7.201e+00 0.00000 Inf
ORdate_year2012 2.179e-01 4.589e+00 0.00000 Inf
ORdate_year2013 2.412e-01 4.147e+00 0.00000 Inf
ORdate_year2014 NA NA NA NA
ORdate_year2015 2.045e-01 4.891e+00 0.00000 Inf
ORdate_year2016 NA NA NA NA
ORdate_year2017 NA NA NA NA
ORdate_year2018 NA NA NA NA
ORdate_year2019 NA NA NA NA
ORdate_year2020 NA NA NA NA
ORdate_year2021 NA NA NA NA
ORdate_year2022 NA NA NA NA
Hypertension.compositeno 4.533e-09 2.206e+08 0.00000 Inf
Hypertension.compositeyes NA NA NA NA
DiabetesStatusDiabetes 3.098e+00 3.228e-01 1.23046 7.7989
SmokerStatusEx-smoker 4.512e-01 2.217e+00 0.17810 1.1429
SmokerStatusNever smoked 2.887e-01 3.464e+00 0.05663 1.4720
Med.Statin.LLDno 1.725e+00 5.798e-01 0.68814 4.3233
Med.Statin.LLDyes NA NA NA NA
Med.all.antiplateletno 2.769e+00 3.612e-01 0.88349 8.6756
Med.all.antiplateletyes NA NA NA NA
GFR_MDRD 9.749e-01 1.026e+00 0.95309 0.9972
BMI 1.033e+00 9.681e-01 0.92039 1.1592
MedHx_CVDNo 5.004e-01 1.998e+00 0.17351 1.4432
stenose0-49% 5.082e-17 1.968e+16 0.00000 Inf
stenose50-70% 9.330e-09 1.072e+08 0.00000 Inf
stenose70-90% 8.585e-09 1.165e+08 0.00000 Inf
stenose90-99% 9.764e-09 1.024e+08 0.00000 Inf
stenose100% (Occlusion) NA NA NA NA
stenoseNA NA NA NA NA
stenose50-99% NA NA NA NA
stenose70-99% NA NA NA NA
stenose99 NA NA NA NA
Concordance= 0.851 (se = 0.032 )
Likelihood ratio test= 47.16 on 29 df, p=0.02
Wald test = 22.47 on 29 df, p=0.8
Score (logrank) test = 41.3 on 29 df, p=0.06
> writing the Cox-regression fashizzle to Excel...
Summarizing Cox regression results for ' CD36 ' and its association to ' epcvdeath.3years ' in ' AERNASE.clin.targets '.
Collecting data.
We have collected the following:
Dataset used..............: AERNASE.clin.targets
Outcome analyzed..........: epcvdeath.3years
Protein...................: CD36
Effect size...............: 0.388061
Standard error............: 0.448223
Odds ratio (effect size)..: 1.474
Lower 95% CI..............: 0.612
Upper 95% CI..............: 3.549
T-value...................: 0.865777
P-value...................: 0.3866123
Sample size in model......: 540
Number of events..........: 24

cat("- Edit the column names...\n")
- Edit the column names...
colnames(COX.results) = c("Dataset", "Outcome", "CpG",
"Beta", "s.e.m.",
"HR", "low95CI", "up95CI",
"Z-value", "P-value", "SampleSize", "N_events")
cat("- Correct the variable types...\n")
- Correct the variable types...
COX.results$Beta <- as.numeric(COX.results$Beta)
COX.results$s.e.m. <- as.numeric(COX.results$s.e.m.)
COX.results$HR <- as.numeric(COX.results$HR)
COX.results$low95CI <- as.numeric(COX.results$low95CI)
COX.results$up95CI <- as.numeric(COX.results$up95CI)
COX.results$`Z-value` <- as.numeric(COX.results$`Z-value`)
COX.results$`P-value` <- as.numeric(COX.results$`P-value`)
COX.results$SampleSize <- as.numeric(COX.results$SampleSize)
COX.results$N_events <- as.numeric(COX.results$N_events)
AERNASE.clin.targets.COX.results <- COX.results
# Save the data
cat("- Writing results to Excel-file...\n")
- Writing results to Excel-file...
head.style <- createStyle(textDecoration = "BOLD")
write.xlsx(AERNASE.clin.targets.COX.results,
file = paste0(OUT_loc, "/",Today,".AERNASE.clin.targets.Cox.2G.MODEL2.xlsx"),
creator = "Sander W. van der Laan",
sheetName = "Results", headerStyle = head.style,
rowNames = FALSE, colNames = TRUE, overwrite = TRUE)
# Removing intermediates
cat("- Removing intermediate files...\n")
- Removing intermediate files...
rm(TEMP.DF, target_of_interest, fit, cox, coxplot, COX.results, COX.results.TEMP, head.style, AERNASE.clin.targets.COX.results)